1
|
Uparela-Reyes MJ, Villegas-Trujillo LM, Cespedes J, Velásquez-Vera M, Rubiano AM. Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review. World Neurosurg 2024; 188:83-92. [PMID: 38759786 DOI: 10.1016/j.wneu.2024.05.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and mini-review on the main uses that have been developed for TBI in AI. METHODS The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of key words, countries co-authorship, and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies. RESULTS A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and PLOS ONE were those with the greatest number of publications. The most frequently co-occurring key words were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles. CONCLUSIONS AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.
Collapse
Affiliation(s)
- Maria José Uparela-Reyes
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia.
| | - Lina María Villegas-Trujillo
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; School of Biomedical Sciences, Universidad del Valle, Cali, Colombia
| | - Jorge Cespedes
- Comprehensive Epilepsy Center, Yale University, New Haven, Connecticut, USA
| | - Miguel Velásquez-Vera
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia
| | - Andrés M Rubiano
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia; INUB-Meditech Research Group, Neurosciences Institute, Universidad El Bosque, Bogotá, Colombia
| |
Collapse
|
2
|
Tiruneh SA, Vu TTT, Rolnik DL, Teede HJ, Enticott J. Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review. Curr Hypertens Rep 2024; 26:309-323. [PMID: 38806766 PMCID: PMC11199280 DOI: 10.1007/s11906-024-01297-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2024] [Indexed: 05/30/2024]
Abstract
PURPOSE OF REVIEW Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia. RECENT FINDINGS From 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.
Collapse
Affiliation(s)
- Sofonyas Abebaw Tiruneh
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Tra Thuan Thanh Vu
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Daniel Lorber Rolnik
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
| | - Helena J Teede
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Joanne Enticott
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
| |
Collapse
|
3
|
Alimohammadi E, Fatahi E, Abdi A, Reza Bagheri S. Assessing the predictive capability of machine learning models in determining clinical outcomes for patients with cervical spondylotic myelopathy treated with laminectomy and posterior spinal fusion. Patient Saf Surg 2024; 18:21. [PMID: 38844999 PMCID: PMC11155139 DOI: 10.1186/s13037-024-00403-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 05/27/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND Cervical spondylotic myelopathy (CSM) is a prevalent degenerative condition resulting from spinal cord compression and injury. Laminectomy with posterior spinal fusion (LPSF) is a commonly employed treatment approach for CSM patients. This study aimed to assess the effectiveness of machine learning models (MLMs) in predicting clinical outcomes in CSM patients undergoing LPSF. METHODS A retrospective analysis was conducted on 329 CSM patients who underwent LPSF at our institution from Jul 2017 to Jul 2023. Neurological outcomes were evaluated using the modified Japanese Orthopaedic Association (mJOA) scale preoperatively and at the final follow-up. Patients were categorized into two groups based on clinical outcomes: the favorable group (recovery rates ≥ 52.8%) and the unfavorable group (recovery rates < 52.8%). Potential predictors for poor clinical outcomes were compared between the groups. Four MLMs-random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighborhood (k-NN)-were utilized to predict clinical outcome. RF model was also employed to identify factors associated with poor clinical outcome. RESULTS Out of the 329 patients, 185 were male (56.2%) and 144 were female (43.4%), with an average follow-up period of 17.86 ± 1.74 months. Among them, 267 patients (81.2%) had favorable clinical outcomes, while 62 patients (18.8%) did not achieve favorable results. Analysis using binary logistic regression indicated that age, preoperative mJOA scale, and symptom duration (p < 0.05) were independent predictors of unfavorable clinical outcomes. All models performed satisfactorily, with RF achieving the highest accuracy of 0.922. RF also displayed superior sensitivity and specificity (sensitivity = 0.851, specificity = 0.944). The Area under the Curve (AUC) values for RF, Logistic LR, SVM, and k-NN were 0.905, 0.827, 0.851, and 0.883, respectively. The RF model identified preoperative mJOA scale, age, symptom duration, and MRI signal changes as the most significant variables associated with poor clinical outcomes in descending order. CONCLUSIONS This study highlighted the effectiveness of machine learning models in predicting the clinical outcomes of CSM patients undergoing LPSF. These models have the potential to forecast clinical outcomes in this patient population, providing valuable prognostic insights for preoperative counseling and postoperative management.
Collapse
Affiliation(s)
- Ehsan Alimohammadi
- Department of Neurosurgery, Kermanshah University of Medical Sciences, Imam Reza hospital, Kermanshah, Iran.
| | - Elnaz Fatahi
- Kermanshah University of Medical Sciences, Imam Reza hospital, Kermanshah, Iran
| | - Alireza Abdi
- Kermanshah University of Medical Sciences, Imam Reza hospital, Kermanshah, Iran
| | - Seyed Reza Bagheri
- Department of Neurosurgery, Kermanshah University of Medical Sciences, Imam Reza hospital, Kermanshah, Iran
| |
Collapse
|
4
|
Muehlschlegel S. Prognostication in Neurocritical Care. Continuum (Minneap Minn) 2024; 30:878-903. [PMID: 38830074 DOI: 10.1212/con.0000000000001433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
OBJECTIVE This article synthesizes the current literature on prognostication in neurocritical care, identifies existing challenges, and proposes future research directions to reduce variability and enhance scientific and patient-centered approaches to neuroprognostication. LATEST DEVELOPMENTS Patients with severe acute brain injury often lack the capacity to make their own medical decisions, leaving surrogate decision makers responsible for life-or-death choices. These decisions heavily rely on clinicians' prognostication, which is still considered an art because of the previous lack of specific guidelines. Consequently, there is significant variability in neuroprognostication practices. This article examines various aspects of neuroprognostication. It explores the cognitive approach to prognostication, highlights the use of statistical modeling such as Bayesian models and machine learning, emphasizes the importance of clinician-family communication during prognostic disclosures, and proposes shared decision making for more patient-centered care. ESSENTIAL POINTS This article identifies ongoing challenges in the field and emphasizes the need for future research to ameliorate variability in neuroprognostication. By focusing on scientific methodologies and patient-centered approaches, this research aims to provide guidance and tools that may enhance neuroprognostication in neurocritical care.
Collapse
|
5
|
Clift AK, Tan PS, Patone M, Liao W, Coupland C, Bashford-Rogers R, Sivakumar S, Hippisley-Cox J. Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model. Br J Cancer 2024; 130:1969-1978. [PMID: 38702436 PMCID: PMC11183048 DOI: 10.1038/s41416-024-02693-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways. METHODS Population-based cohort study of adults aged 30-85 years at type 2 diabetes diagnosis (2010-2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal-external cross-validation. RESULTS Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell's C-index 0.802 (95% CI: 0.797-0.817)), the highest clinical utility, and was well calibrated. The model's highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity. DISCUSSION A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging.
Collapse
Affiliation(s)
- Ash Kieran Clift
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Cancer Research UK Oxford Centre, University of Oxford, Oxford, UK
| | - Pui San Tan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Martina Patone
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Weiqi Liao
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Carol Coupland
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Centre for Academic Primary Care, School of Medicine, University of Nottingham, Nottingham, UK
| | - Rachael Bashford-Rogers
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - Shivan Sivakumar
- Institute of Immunology and Immunotherapy, Birmingham Medical School, Birmingham, UK
- Cancer Centre, Queen Elizabeth Hospital, University Hospitals of Birmingham NHS Trust, Birmingham, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| |
Collapse
|
6
|
Barrett-Jolley R, German AJ. Variables associated with owner perceptions of the health of their dog: Further analysis of data from a large international survey. PLoS One 2024; 19:e0280173. [PMID: 38748734 PMCID: PMC11095744 DOI: 10.1371/journal.pone.0280173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/30/2024] [Indexed: 05/19/2024] Open
Abstract
In a recent study (doi: 10.1371/journal.pone.0265662), associations were identified between owner-reported dog health status and diet, whereby those fed a vegan diet were perceived to be healthier. However, the study was limited because it did not consider possible confounding from variables not included in the analysis. The aim of the current study was to extend these earlier findings, using different modelling techniques and including multiple variables, to identify the most important predictors of owner perceptions of dog health. From the original dataset, two binary outcome variables were created: the 'any health problem' distinguished dogs that owners perceived to be healthy ("no") from those perceived to have illness of any severity; the 'significant illness' variable distinguished dogs that owners perceived to be either healthy or having mild illness ("no") from those perceived to have significant or serious illness ("yes"). Associations between these health outcomes and both owner-animal metadata and healthcare variables were assessed using logistic regression and machine learning predictive modelling using XGBoost. For the any health problem outcome, best-fit models for both logistic regression (area under curve [AUC] 0.842) and XGBoost (AUC 0.836) contained the variables dog age, veterinary visits and received medication, whilst owner age and breed size category also featured. For the significant illness outcome, received medication, veterinary visits, dog age and were again the most important predictors for both logistic regression (AUC 0.903) and XGBoost (AUC 0.887), whilst breed size category, education and owner age also featured in the latter. Any contribution from the dog vegan diet variable was negligible. The results of the current study extend the previous research using the same dataset and suggest that diet has limited impact on owner-perceived dog health status; instead, dog age, frequency of veterinary visits and receiving medication are most important.
Collapse
Affiliation(s)
- Richard Barrett-Jolley
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Alexander J. German
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| |
Collapse
|
7
|
Lee CT, Zhang K, Li W, Tang K, Ling Y, Walji MF, Jiang X. Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach. J Dent 2024; 144:104921. [PMID: 38437976 DOI: 10.1016/j.jdent.2024.104921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 02/17/2024] [Accepted: 03/01/2024] [Indexed: 03/06/2024] Open
Abstract
OBJECTIVES This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting. METHODS Information on periodontitis patients and nineteen factors identified at the initial visit was extracted from electronic health records. The primary outcome is tooth loss phenotype (presence or absence of tooth loss). Prediction models were built on significant factors (single or combinatory) selected by the RuleFit algorithm, and these factors were further adopted by regression models. Model performance was evaluated by Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC). Associations between predictors and the tooth loss phenotype were also evaluated by classical statistical approaches to validate the performance of machine learning models. RESULTS In total, 7840 patients were included. The machine learning model predicting the tooth loss phenotype achieved AUROC of 0.71 and AUPRC of 0.66. Age, periodontal diagnosis, number of missing teeth at baseline, furcation involvement, and tooth mobility were associated with the tooth loss phenotype in both machine learning and classical statistical models. CONCLUSIONS The rule-based machine learning approach improves model explainability compared to classical statistical methods. However, the model's generalizability needs to be further validated by external datasets. CLINICAL SIGNIFICANCE Predictors identified by the current machine learning approach using the RuleFit algorithm had clinically relevant thresholds in predicting the tooth loss phenotype in a large and diverse periodontitis patient cohort. The results of this study will assist clinicians in performing risk assessment for periodontitis at the initial visit.
Collapse
Affiliation(s)
- Chun-Teh Lee
- Department of Periodontics and Dental Hygiene, The University of Texas Health Science Center at Houston School of Dentistry, 7500 Cambridge Street, Houston, TX 77054, USA
| | - Kai Zhang
- The University of Texas Health Science Center at Houston School of Biomedical Informatics, 7000 Fannin St, Houston, Texas 77030, USA
| | - Wen Li
- Division of Clinical and Translational Sciences, Department of Internal Medicine, the University of Texas McGovern Medical School at Houston, 6431 Fannin St, Houston, Texas, USA; Biostatistics/Epidemiology/Research Design (BERD) Component, Center for Clinical and Translational Sciences (CCTS), University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, Houston, Texas 77030, USA
| | - Kaichen Tang
- The University of Texas Health Science Center at Houston School of Biomedical Informatics, 7000 Fannin St, Houston, Texas 77030, USA
| | - Yaobin Ling
- The University of Texas Health Science Center at Houston School of Biomedical Informatics, 7000 Fannin St, Houston, Texas 77030, USA
| | - Muhammad F Walji
- The University of Texas Health Science Center at Houston School of Biomedical Informatics, 7000 Fannin St, Houston, Texas 77030, USA; Department of Diagnostic and Biomedical Sciences, The University of Texas Health Science Center at Houston School of Dentistry, 7000 Fannin St., Houston, Texas 77030, USA
| | - Xiaoqian Jiang
- The University of Texas Health Science Center at Houston School of Biomedical Informatics, 7000 Fannin St, Houston, Texas 77030, USA.
| |
Collapse
|
8
|
Fitzgerald M, Ponsford JL, Hill R, Rushworth N, Kendall E, Armstrong E, Gilroy J, Bullen J, Keeves J, Bagg MK, Hellewell SC, Lannin NA, O'Brien TJ, Cameron PA, Cooper DJ, Gabbe BJ. The Australian Traumatic Brain Injury Initiative: Single Data Dictionary to Predict Outcome for People With Moderate-Severe Traumatic Brain Injury. J Neurotrauma 2024. [PMID: 38117144 DOI: 10.1089/neu.2023.0467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023] Open
Abstract
In this series of eight articles, the Australian Traumatic Brain Injury Initiative (AUS-TBI) consortium describes the Australian approach used to select the common data elements collected acutely that have been shown to predict outcome following moderate-severe traumatic brain injury (TBI) across the lifespan. This article presents the unified single data dictionary, together with additional measures chosen to facilitate comparative effectiveness research and data linkage. Consultations with the AUS-TBI Lived Experience Expert Group provided insights on the merits and considerations regarding data elements for some of the study areas, as well as more general principles to guide the collection of data and the selection of meaningful measures. These are presented as a series of guiding principles and themes. The AUS-TBI Aboriginal and Torres Strait Islander Advisory Group identified a number of key points and considerations for the project approach specific to Aboriginal and Torres Strait Islander peoples, including key issues of data sovereignty and community involvement. These are outlined in the form of principles to guide selection of appropriate methodologies, data management, and governance. Implementation of the AUS-TBI approach aims to maximize ongoing data collection and linkage, to facilitate personalization of care and improved outcomes for people who experience moderate-severe TBI.
Collapse
Affiliation(s)
- Melinda Fitzgerald
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
| | - Jennie L Ponsford
- School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Epworth Healthcare, Melbourne, Victoria, Australia
| | - Regina Hill
- Regina Hill Effective Consulting Pty. Ltd., Melbourne, Victoria, Australia
| | - Nick Rushworth
- Brain Injury Australia, Sydney, New South Wales, Australia
| | - Elizabeth Kendall
- The Hopkins Centre, Griffith University, Brisbane, Queensland, Australia
| | - Elizabeth Armstrong
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
| | - John Gilroy
- Aboriginal and Torres Strait Islander Research, Faculty of Medicine and Health, The University of Sydney, Sydney New South Wales, Australia
| | - Jonathan Bullen
- Office of DVCA, Curtin University, Bentley, Western Australia, Australia
- Telethon Kids Institute, West Perth, Western Australia, Australia
| | - Jemma Keeves
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Matthew K Bagg
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Health Sciences, University of Notre Dame Australia, Fremantle, Western Australia, Australia
| | - Sarah C Hellewell
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
| | - Natasha A Lannin
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
- Alfred Health, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Peter A Cameron
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- National Trauma Research Institute, Melbourne, Victoria, Australia
- Emergency and Trauma Centre, The Alfred Hospital, Melbourne, Victoria, Australia
| | - D Jamie Cooper
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
- Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Belinda J Gabbe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Health Data Research UK, Swansea University Medical School, Swansea University, Singleton Park, United Kingdom
| |
Collapse
|
9
|
Kyriacou C, Ledger A, Bobdiwala S, Ayim F, Kirk E, Abughazza O, Guha S, Vathanan V, Gould D, Timmerman D, Van Calster B, Bourne T. Updating M6 pregnancy of unknown location risk-prediction model including evaluation of clinical factors. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 63:408-418. [PMID: 37842861 DOI: 10.1002/uog.27515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/19/2023] [Accepted: 10/05/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVES Ectopic pregnancy (EP) is a major high-risk outcome following a pregnancy of unknown location (PUL) classification. Biochemical markers are used to triage PUL as high vs low risk to guide appropriate follow-up. The M6 model is currently the best risk-prediction model. We aimed to update the M6 model and evaluate whether performance can be improved by including clinical factors. METHODS This prospective cohort study recruited consecutive PUL between January 2015 and January 2017 at eight units (Phase 1), with two centers continuing recruitment between January 2017 and March 2021 (Phase 2). Serum samples were collected routinely and sent for β-human chorionic gonadotropin (β-hCG) and progesterone measurement. Clinical factors recorded were maternal age, pain score, bleeding score and history of EP. Based on transvaginal ultrasonography and/or biochemical confirmation during follow-up, PUL were classified subsequently as failed PUL (FPUL), intrauterine pregnancy (IUP) or EP (including persistent PUL (PPUL)). The M6 models with (M6P ) and without (M6NP ) progesterone were refitted and extended with clinical factors. Model validation was performed using internal-external cross-validation (IECV) (Phase 1) and temporal external validation (EV) (Phase 2). Missing values were handled using multiple imputation. RESULTS Overall, 5473 PUL were recruited over both phases. A total of 709 PUL were excluded because maternal age was < 16 years or initial β-hCG was ≤ 25 IU/L, leaving 4764 (87%) PUL for analysis (2894 in Phase 1 and 1870 in Phase 2). For the refitted M6P model, the area under the receiver-operating-characteristics curve (AUC) for EP/PPUL vs IUP/FPUL was 0.89 for IECV and 0.84-0.88 for EV, with respective sensitivities of 94% and 92-93%. For the refitted M6NP model, the AUCs were 0.85 for IECV and 0.82-0.86 for EV, with respective sensitivities of 92% and 93-94%. Calibration performance was good overall, but with heterogeneity between centers. Net Benefit confirmed clinical utility. The change in AUC when M6P was extended to include maternal age, bleeding score and history of EP was between -0.02 and 0.01, depending on center and phase. The corresponding change in AUC when M6NP was extended was between -0.01 and 0.03. At the 5% threshold to define high risk of EP/PPUL, extending M6P altered sensitivity by -0.02 to -0.01, specificity by 0.03 to 0.04 and Net Benefit by -0.005 to 0.006. Extending M6NP altered sensitivity by -0.03 to -0.01, specificity by 0.05 to 0.07 and Net Benefit by -0.005 to 0.006. CONCLUSIONS The updated M6 model offers accurate diagnostic performance, with excellent sensitivity for EP. Adding clinical factors to the model improved performance in some centers, especially when progesterone levels were not suitable or unavailable. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
Collapse
Affiliation(s)
- C Kyriacou
- Tommy's National Centre for Miscarriage Research, Queen Charlotte's and Chelsea Hospital, Imperial College London, London, UK
| | - A Ledger
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - S Bobdiwala
- Tommy's National Centre for Miscarriage Research, Queen Charlotte's and Chelsea Hospital, Imperial College London, London, UK
| | - F Ayim
- Department of Gynaecology, Hillingdon Hospital NHS Trust, London, UK
| | - E Kirk
- Department of Gynaecology, Royal Free NHS Foundation Trust, London, UK
| | - O Abughazza
- Department of Gynaecology, Royal Surrey County Hospital, Guildford, UK
| | - S Guha
- Department of Gynaecology, Chelsea and Westminster NHS Trust, London, UK
| | - V Vathanan
- Department of Gynaecology, Wexham Park Hospital, London, UK
| | - D Gould
- Department of Gynaecology, St Mary's Hospital, London, UK
| | - D Timmerman
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Gynecology, University Hospital Leuven, Leuven, Belgium
| | - B Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - T Bourne
- Tommy's National Centre for Miscarriage Research, Queen Charlotte's and Chelsea Hospital, Imperial College London, London, UK
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Gynecology, University Hospital Leuven, Leuven, Belgium
| |
Collapse
|
10
|
Berg B, Gorosito MA, Fjeld O, Haugerud H, Storheim K, Solberg TK, Grotle M. Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery. JAMA Netw Open 2024; 7:e2355024. [PMID: 38324310 PMCID: PMC10851101 DOI: 10.1001/jamanetworkopen.2023.55024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/14/2023] [Indexed: 02/08/2024] Open
Abstract
Importance Lumber disc herniation surgery can reduce pain and disability. However, a sizable minority of individuals experience minimal benefit, necessitating the development of accurate prediction models. Objective To develop and validate prediction models for disability and pain 12 months after lumbar disc herniation surgery. Design, Setting, and Participants A prospective, multicenter, registry-based prognostic study was conducted on a cohort of individuals undergoing lumbar disc herniation surgery from January 1, 2007, to May 31, 2021. Patients in the Norwegian Registry for Spine Surgery from all public and private hospitals in Norway performing spine surgery were included. Data analysis was performed from January to June 2023. Exposures Microdiscectomy or open discectomy. Main Outcomes and Measures Treatment success at 12 months, defined as improvement in Oswestry Disability Index (ODI) of 22 points or more; Numeric Rating Scale (NRS) back pain improvement of 2 or more points, and NRS leg pain improvement of 4 or more points. Machine learning models were trained for model development and internal-external cross-validation applied over geographic regions to validate the models. Model performance was assessed through discrimination (C statistic) and calibration (slope and intercept). Results Analysis included 22 707 surgical cases (21 161 patients) (ODI model) (mean [SD] age, 47.0 [14.0] years; 12 952 [57.0%] males). Treatment nonsuccess was experienced by 33% (ODI), 27% (NRS back pain), and 31% (NRS leg pain) of the patients. In internal-external cross-validation, the selected machine learning models showed consistent discrimination and calibration across all 5 regions. The C statistic ranged from 0.81 to 0.84 (pooled random-effects meta-analysis estimate, 0.82; 95% CI, 0.81-0.84) for the ODI model. Calibration slopes (point estimates, 0.94-1.03; pooled estimate, 0.99; 95% CI, 0.93-1.06) and calibration intercepts (point estimates, -0.05 to 0.11; pooled estimate, 0.01; 95% CI, -0.07 to 0.10) were also consistent across regions. For NRS back pain, the C statistic ranged from 0.75 to 0.80 (pooled estimate, 0.77; 95% CI, 0.75-0.79); for NRS leg pain, the C statistic ranged from 0.74 to 0.77 (pooled estimate, 0.75; 95% CI, 0.74-0.76). Only minor heterogeneity was found in calibration slopes and intercepts. Conclusion The findings of this study suggest that the models developed can inform patients and clinicians about individual prognosis and aid in surgical decision-making.
Collapse
Affiliation(s)
- Bjørnar Berg
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Division of Orthopedic Surgery, Oslo University Hospital, Oslo, Norway
| | - Martin A. Gorosito
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
| | - Olaf Fjeld
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Hårek Haugerud
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
| | - Kjersti Storheim
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Division of Clinical Neuroscience, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Tore K. Solberg
- Institute of Clinical Medicine, The Artic University of Norway, Tromsø, Norway
- The Norwegian Registry for Spine Surgery, The University Hospital of North Norway, Tromsø, Norway
| | - Margreth Grotle
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Division of Clinical Neuroscience, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| |
Collapse
|
11
|
Irie F, Matsumoto K, Matsuo R, Nohara Y, Wakisaka Y, Ago T, Nakashima N, Kitazono T, Kamouchi M. Predictive Performance of Machine Learning-Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study. JMIR AI 2024; 3:e46840. [PMID: 38875590 PMCID: PMC11041492 DOI: 10.2196/46840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 10/30/2023] [Accepted: 12/04/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Although machine learning is a promising tool for making prognoses, the performance of machine learning in predicting outcomes after stroke remains to be examined. OBJECTIVE This study aims to examine how much data-driven models with machine learning improve predictive performance for poststroke outcomes compared with conventional stroke prognostic scores and to elucidate how explanatory variables in machine learning-based models differ from the items of the stroke prognostic scores. METHODS We used data from 10,513 patients who were registered in a multicenter prospective stroke registry in Japan between 2007 and 2017. The outcomes were poor functional outcome (modified Rankin Scale score >2) and death at 3 months after stroke. Machine learning-based models were developed using all variables with regularization methods, random forests, or boosted trees. We selected 3 stroke prognostic scores, namely, ASTRAL (Acute Stroke Registry and Analysis of Lausanne), PLAN (preadmission comorbidities, level of consciousness, age, neurologic deficit), and iScore (Ischemic Stroke Predictive Risk Score) for comparison. Item-based regression models were developed using the items of these 3 scores. The model performance was assessed in terms of discrimination and calibration. To compare the predictive performance of the data-driven model with that of the item-based model, we performed internal validation after random splits of identical populations into 80% of patients as a training set and 20% of patients as a test set; the models were developed in the training set and were validated in the test set. We evaluated the contribution of each variable to the models and compared the predictors used in the machine learning-based models with the items of the stroke prognostic scores. RESULTS The mean age of the study patients was 73.0 (SD 12.5) years, and 59.1% (6209/10,513) of them were men. The area under the receiver operating characteristic curves and the area under the precision-recall curves for predicting poststroke outcomes were higher for machine learning-based models than for item-based models in identical populations after random splits. Machine learning-based models also performed better than item-based models in terms of the Brier score. Machine learning-based models used different explanatory variables, such as laboratory data, from the items of the conventional stroke prognostic scores. Including these data in the machine learning-based models as explanatory variables improved performance in predicting outcomes after stroke, especially poststroke death. CONCLUSIONS Machine learning-based models performed better in predicting poststroke outcomes than regression models using the items of conventional stroke prognostic scores, although they required additional variables, such as laboratory data, to attain improved performance. Further studies are warranted to validate the usefulness of machine learning in clinical settings.
Collapse
Affiliation(s)
- Fumi Irie
- Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Koutarou Matsumoto
- Biostatistics Center, Graduate School of Medicine, Kurume University, Kurume, Japan
| | - Ryu Matsuo
- Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yasunobu Nohara
- Big Data Science and Technology, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
| | - Yoshinobu Wakisaka
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tetsuro Ago
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naoki Nakashima
- Medical Information Center, Kyushu University Hospital, Fukuoka, Japan
| | - Takanari Kitazono
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahiro Kamouchi
- Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| |
Collapse
|
12
|
Held U, Forzy T, Signorell A, Deforth M, Burgstaller JM, Wertli MM. Development and internal validation of a prediction model for long-term opioid use-an analysis of insurance claims data. Pain 2024; 165:44-53. [PMID: 37782553 PMCID: PMC10723645 DOI: 10.1097/j.pain.0000000000003023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 10/04/2023]
Abstract
ABSTRACT In the United States, a public-health crisis of opioid overuse has been observed, and in Europe, prescriptions of opioids are strongly increasing over time. The objective was to develop and validate a multivariable prognostic model to be used at the beginning of an opioid prescription episode, aiming to identify individual patients at high risk for long-term opioid use based on routinely collected data. Predictors including demographics, comorbid diseases, comedication, morphine dose at episode initiation, and prescription practice were collected. The primary outcome was long-term opioid use, defined as opioid use of either >90 days duration and ≥10 claims or >120 days, independent of the number of claims. Traditional generalized linear statistical regression models and machine learning approaches were applied. The area under the curve, calibration plots, and the scaled Brier score assessed model performance. More than four hundred thousand opioid episodes were included. The final risk prediction model had an area under the curve of 0.927 (95% confidence interval 0.924-0.931) in the validation set, and this model had a scaled Brier score of 48.5%. Using a threshold of 10% predicted probability to identify patients at high risk, the overall accuracy of this risk prediction model was 81.6% (95% confidence interval 81.2% to 82.0%). Our study demonstrated that long-term opioid use can be predicted at the initiation of an opioid prescription episode, with satisfactory accuracy using data routinely collected at a large health insurance company. Traditional statistical methods resulted in higher discriminative ability and similarly good calibration as compared with machine learning approaches.
Collapse
Affiliation(s)
- Ulrike Held
- Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Tom Forzy
- Master Program Statistics, ETH Zurich, Zurich, Switzerland
| | - Andri Signorell
- Department of Health Sciences, Helsana, Dübendorf, Switzerland
| | - Manja Deforth
- Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Jakob M. Burgstaller
- Institute of Primary Care, University and University Hospital Zurich, Zurich, Switzerland
| | - Maria M. Wertli
- Department of Internal Medicine, Cantonal Hospital Baden KSB, Baden, Switzerland
- Department of General Internal Medicine University Hospital Bern, University of Bern, Switzerland
| |
Collapse
|
13
|
Chekol YM, Merid MW, Tesema GA, Tesfie TK, Tebeje TM, Gelaw NB, Gebi NB, Seretew WS. Development and Validation of a Risk Prediction Model to Estimate the Risk of Stroke Among Hypertensive Patients in University of Gondar Comprehensive Specialized Hospital, Gondar, 2012 to 2022. Degener Neurol Neuromuscul Dis 2023; 13:89-110. [PMID: 38116193 PMCID: PMC10728309 DOI: 10.2147/dnnd.s435806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
Background A risk prediction model to predict the risk of stroke has been developed for hypertensive patients. However, the discriminating power is poor, and the predictors are not easily accessible in low-income countries. Therefore, developing a validated risk prediction model to estimate the risk of stroke could help physicians to choose optimal treatment and precisely estimate the risk of stroke. Objective This study aims to develop and validate a risk prediction model to estimate the risk of stroke among hypertensive patients at the University of Gondar Comprehensive Specialized Hospital. Methods A retrospective follow-up study was conducted among 743 hypertensive patients between September 01/2012 and January 31/2022. The participants were selected using a simple random sampling technique. Model performance was evaluated using discrimination, calibration, and Brier scores. Internal validity and clinical utility were evaluated using bootstrapping and a decision curve analysis. Results Incidence of stroke was 31.4 per 1000 person-years (95% CI: 26.0, 37.7). Combinations of six predictors were selected for model development (sex, residence, baseline diastolic blood pressure, comorbidity, diabetes, and uncontrolled hypertension). In multivariable logistic regression, the discriminatory power of the model was 0.973 (95% CI: 0.959, 0.987). Calibration plot illustrated an overlap between the probabilities of the predicted and actual observed risks after 10,000 times bootstrap re-sampling, with a sensitivity of 92.79%, specificity 93.51%, and accuracy of 93.41%. The decision curve analysis demonstrated that the net benefit of the model was better than other intervention strategies, starting from the initial point. Conclusion An internally validated, accurate prediction model was developed and visualized in a nomogram. The model is then changed to an offline mobile web-based application to facilitate clinical applicability. The authors recommend that other researchers eternally validate the model.
Collapse
Affiliation(s)
- Yazachew Moges Chekol
- Department of Health Information Technician, Mizan Aman College of Health Science, Mizan Aman, Ethiopia
| | | | | | - Tigabu Kidie Tesfie
- Department of Public Health, Debre Markos University, Debre Markos, Ethiopia
| | - Tsion Mulat Tebeje
- Department of Epidemiology and Biostatistics, Dilla University, Dilla, Ethiopia
| | | | | | - Wullo Sisay Seretew
- Department of Epidemiology and Biostatistics, University of Gondar, Gondar, Ethiopia
| |
Collapse
|
14
|
Gobert F, Corneyllie A, Bastuji H, Berthomier C, Thevenet M, Abernot J, Raverot V, Dailler F, Guérin C, Gronfier C, Luauté J, Perrin F. Twenty-four-hour rhythmicities in disorders of consciousness are associated with a favourable outcome. Commun Biol 2023; 6:1213. [PMID: 38030756 PMCID: PMC10687012 DOI: 10.1038/s42003-023-05588-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 11/15/2023] [Indexed: 12/01/2023] Open
Abstract
Fluctuations of consciousness and their rhythmicities have been rarely studied in patients with a disorder of consciousness after acute brain injuries. 24-h assessment of brain (EEG), behaviour (eye-opening), and circadian (clock-controlled hormones secretion from urine) functions was performed in acute brain-injured patients. The distribution, long-term predictability, and rhythmicity (circadian/ultradian) of various EEG features were compared with the initial clinical status, the functional outcome, and the circadian rhythmicities of behaviour and clock-controlled hormones. Here we show that more physiological and favourable patterns of fluctuations are associated with a higher 24 h predictability and sharp up-and-down shape of EEG switches, reminiscent of the Flip-Flop model of sleep. Multimodal rhythmic analysis shows that patients with simultaneous circadian rhythmicity for brain, behaviour, and hormones had a favourable outcome. Finally, both re-emerging EEG fluctuations and homogeneous 24-h cycles for EEG, eye-opening, and hormones appeared as surrogates for preserved functionality in brainstem and basal forebrain, which are key prognostic factors for later improvement. While the recovery of consciousness has previously been related to a high short-term complexity, we suggest in this exploratory study the importance of the high predictability of the 24 h long-term generation of brain rhythms and highlight the importance of circadian body-brain rhythms in awakening.
Collapse
Affiliation(s)
- Florent Gobert
- Neuro-Intensive care unit, Hospices Civils de Lyon, Neurological hospital Pierre-Wertheimer, 59 Boulevard Pinel, Bron, France.
- Trajectoires Team, Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), Bâtiment Inserm 16 avenue Doyen Lépine, Bron, France.
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France.
| | - Alexandra Corneyllie
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France
| | - Hélène Bastuji
- Sleep medicine centre, Hospices Civils de Lyon, Bron, F-69677, France
- Neuropain Team, Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 59 Boulevard Pinel, Bron, France
| | | | - Marc Thevenet
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France
| | - Jonas Abernot
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France
| | - Véronique Raverot
- Hormone Laboratory, Hospices Civils de Lyon, Neurological hospital Pierre-Wertheimer, 59 Boulevard Pinel, Bron, France
| | - Frédéric Dailler
- Neuro-Intensive care unit, Hospices Civils de Lyon, Neurological hospital Pierre-Wertheimer, 59 Boulevard Pinel, Bron, France
| | - Claude Guérin
- Intensive care unit, Hospices Civils de Lyon, Croix-Rousse hospital, 103 Grande-Rue de la Croix-Rousse, Lyon, France
- Intensive care unit, Hospices Civils de Lyon, Édouard Herriot hospital, 5 Place d'Arsonval, 69003, Lyon, France
| | - Claude Gronfier
- Waking team (Integrative Physiology of the Brain Arousal Systems), Lyon Neuroscience Research Centre, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Jacques Luauté
- Trajectoires Team, Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), Bâtiment Inserm 16 avenue Doyen Lépine, Bron, France
- Neuro-rehabilitation unit, Hospices Civils de Lyon, Neurological hospital Pierre-Wertheimer, 59 Boulevard Pinel, Bron, France
| | - Fabien Perrin
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France
| |
Collapse
|
15
|
Kobeissy F, Goli M, Yadikar H, Shakkour Z, Kurup M, Haidar MA, Alroumi S, Mondello S, Wang KK, Mechref Y. Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects. Front Neurol 2023; 14:1288740. [PMID: 38073638 PMCID: PMC10703396 DOI: 10.3389/fneur.2023.1288740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/01/2023] [Indexed: 02/12/2024] Open
Abstract
Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.
Collapse
Affiliation(s)
- Firas Kobeissy
- Department of Neurobiology, School of Medicine, Neuroscience Institute, Atlanta, GA, United States
| | - Mona Goli
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Hamad Yadikar
- Department of Biological Sciences Faculty of Science, Kuwait University, Safat, Kuwait
| | - Zaynab Shakkour
- Department of Pathology and Anatomical Sciences, University of Missouri School of Medicine, Columbia, MO, United States
| | - Milin Kurup
- Alabama College of Osteopathic Medicine, Dothan, AL, United States
| | | | - Shahad Alroumi
- Department of Biological Sciences Faculty of Science, Kuwait University, Safat, Kuwait
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Kevin K. Wang
- Department of Neurobiology, School of Medicine, Neuroscience Institute, Atlanta, GA, United States
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| |
Collapse
|
16
|
Zulbayar S, Mollayeva T, Colantonio A, Chan V, Escobar M. Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study. INTELLIGENCE-BASED MEDICINE 2023; 8:100118. [PMID: 38222038 PMCID: PMC10785655 DOI: 10.1016/j.ibmed.2023.100118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
This work aimed to identify pre-existing health conditions of patients with traumatic brain injury (TBI) and develop predictive models for the first TBI event and its external causes by employing a combination of unsupervised and supervised learning algorithms. We acquired up to five years of pre-injury diagnoses for 488,107 patients with TBI and 488,107 matched control patients who entered the emergency department or acute care hospitals between April 1st, 2002, and March 31st, 2020. Diagnoses were obtained from the Ontario Health Insurance Plan (OHIP) database which contains province-wide claims data by physicians in Ontario, Canada for inpatient and outpatient services. A screening process was conducted on the OHIP diagnostic codes to limit the subsequent analysis to codes that were predictive of TBI, which concluded that 314 codes were significantly associated with TBI. The Latent Dirichlet Allocation (LDA) model was applied to the diagnostic codes and generated an optimal number of 19 topics that concur with published literature but also suggest other unexplored areas. Estimated word-topic probabilities from the LDA model helped us detect pre-morbid conditions among patients with TBI by uncovering the underlying patterns of diagnoses, meanwhile estimated document-topic probabilities were utilized in variable creation as form of a dimension reduction. We created 19 topic scores for each patient in the cohort which were utilized along with socio-demographic factors for Random Forest binary classifier models. Test set performances evaluated using area under the receiver operating characteristic curve (AUC) were: TBI event (AUC = 0.85), external cause of injury: falls (AUC = 0.85), struck by/against (AUC = 0.83), cyclist collision (AUC = 0.76), motor vehicle collision (AUC = 0.83). Our analysis successfully demonstrated the feasibility of using machine learning to predict TBI due to various external causes and identified the most important factors that contribute to this prediction.
Collapse
Affiliation(s)
- Suvd Zulbayar
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- Institute of Health and Policy, Management and Evaluation, University of Toronto, M5T 3M6, Canada
| | - Tatyana Mollayeva
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5G 1V7, Canada
- Acquired Brain Injury Research Lab, Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON M5G 1V7, Canada
- KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON M5G 2A2, Canada
| | - Angela Colantonio
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5G 1V7, Canada
- Acquired Brain Injury Research Lab, Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON M5G 1V7, Canada
- KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON M5G 2A2, Canada
- Institute of Health and Policy, Management and Evaluation, University of Toronto, M5T 3M6, Canada
- ICES, Toronto, ON, M4N 3M5, Canada
| | - Vincy Chan
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5G 1V7, Canada
- Acquired Brain Injury Research Lab, Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON M5G 1V7, Canada
- KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON M5G 2A2, Canada
- Institute of Health and Policy, Management and Evaluation, University of Toronto, M5T 3M6, Canada
| | - Michael Escobar
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| |
Collapse
|
17
|
Matsumoto K, Nohara Y, Sakaguchi M, Takayama Y, Fukushige S, Soejima H, Nakashima N, Kamouchi M. Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study. JMIR Perioper Med 2023; 6:e50895. [PMID: 37883164 PMCID: PMC10636625 DOI: 10.2196/50895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/24/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications. OBJECTIVE The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model. METHODS The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method. RESULTS A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance. CONCLUSIONS The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.
Collapse
Affiliation(s)
| | - Yasunobu Nohara
- Big Data Science and Technology, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
| | - Mikako Sakaguchi
- Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Yohei Takayama
- Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Syota Fukushige
- Department of Inspection, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Hidehisa Soejima
- Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Naoki Nakashima
- Medical Information Center, Kyushu University Hospital, Fukuoka, Japan
| | - Masahiro Kamouchi
- Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| |
Collapse
|
18
|
Chang MC, Kim JK, Park D, Kim JH, Kim CR, Choo YJ. The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review. Healthcare (Basel) 2023; 11:2687. [PMID: 37830724 PMCID: PMC10572243 DOI: 10.3390/healthcare11192687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/27/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023] Open
Abstract
Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.
Collapse
Affiliation(s)
- Min Cheol Chang
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea;
| | - Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si 38541, Republic of Korea;
| | - Donghwi Park
- Department of Rehabilitation Medicine, Daegu Fatima Hospital, Daegu 41199, Republic of Korea;
| | - Jang Hwan Kim
- Department of Rehabilitation Technology, Graduate School of Hanseo University, Seosan, Chungcheongnam-do 31962, Republic of Korea;
| | - Chung Reen Kim
- Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Republic of Korea;
| | - Yoo Jin Choo
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea;
| |
Collapse
|
19
|
Stam WT, Ingwersen EW, Ali M, Spijkerman JT, Kazemier G, Bruns ERJ, Daams F. Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery. Surg Today 2023; 53:1209-1215. [PMID: 36840764 PMCID: PMC10520164 DOI: 10.1007/s00595-023-02662-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023]
Abstract
Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.
Collapse
Affiliation(s)
- Wessel T Stam
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- AGEM Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Erik W Ingwersen
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- AGEM Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Mahsoem Ali
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Jorik T Spijkerman
- Independent Consultant in Computational Intelligence, Amsterdam, The Netherlands
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Emma R J Bruns
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Freek Daams
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands.
| |
Collapse
|
20
|
Van Deynse H, Cools W, De Deken VJ, Depreitere B, Hubloue I, Kimpe E, Moens M, Pien K, Tisseghem E, Van Belleghem G, Putman K. Predicting return to work after traumatic brain injury using machine learning and administrative data. Int J Med Inform 2023; 178:105201. [PMID: 37657205 DOI: 10.1016/j.ijmedinf.2023.105201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/02/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND Accurate patient-specific predictions on return-to-work after traumatic brain injury (TBI) can support both clinical practice and policymaking. The use of machine learning on large administrative data provides interesting opportunities to create such prognostic models. AIM The current study assesses whether return-to-work one year after TBI can be predicted accurately from administrative data. Additionally, this study explores how model performance and feature importance change depending on whether a distinction is made between mild and moderate-to-severe TBI. METHODS This study used a population-based dataset that combined discharge, claims and social security data of patients hospitalized with a TBI in Belgium during the year 2016. The prediction of TBI was attempted with three algorithms, elastic net logistic regression, random forest and gradient boosting and compared in their performance by their accuracy, sensitivity, specificity and area under the receiver operator curve (ROC AUC). RESULTS The distinct modelling algorithms resulted in similar results, with 83% accuracy (ROC AUC 85%) for a binary classification of employed vs. not employed and up to 76% (ROC AUC 82%) for a multiclass operationalization of employment outcome. Modelling mild and moderate-to-severe TBI separately did not result in considerable differences in model performance and feature importance. The features of main importance for return-to-work prediction were related to pre-injury employment. DISCUSSION While clearly offering some information beneficial for predicting return-to-work, administrative data needs to be supplemented with additional information to allow further improvement of patient-specific prognose.
Collapse
Affiliation(s)
- Helena Van Deynse
- Interuniversity Centre for Health Economics Research (I-CHER), Vrije Universiteit Brussel, Brussels, Belgium.
| | - Wilfried Cools
- Support for Quantitative and Qualitative Research (SQUARE), Vrije Universiteit Brussel, Brussels, Belgium
| | - Viktor-Jan De Deken
- Interuniversity Centre for Health Economics Research (I-CHER), Vrije Universiteit Brussel, Brussels, Belgium
| | - Bart Depreitere
- Department of Neurosurgery, Universitair Ziekenhuis Leuven, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Ives Hubloue
- Department of Emergency Medicine, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Eva Kimpe
- Interuniversity Centre for Health Economics Research (I-CHER), Vrije Universiteit Brussel, Brussels, Belgium
| | - Maarten Moens
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Brussels, Belgium; Department of Radiology, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Karen Pien
- Department of Medical Registration, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Ellen Tisseghem
- Interuniversity Centre for Health Economics Research (I-CHER), Vrije Universiteit Brussel, Brussels, Belgium
| | - Griet Van Belleghem
- Interuniversity Centre for Health Economics Research (I-CHER), Vrije Universiteit Brussel, Brussels, Belgium
| | - Koen Putman
- Interuniversity Centre for Health Economics Research (I-CHER), Vrije Universiteit Brussel, Brussels, Belgium
| |
Collapse
|
21
|
Ingwersen EW, Stam WT, Meijs BJV, Roor J, Besselink MG, Groot Koerkamp B, de Hingh IHJT, van Santvoort HC, Stommel MWJ, Daams F. Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy. Surgery 2023; 174:435-440. [PMID: 37150712 DOI: 10.1016/j.surg.2023.03.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/02/2023] [Accepted: 03/20/2023] [Indexed: 05/09/2023]
Abstract
BACKGROUND Machine learning is increasingly advocated to develop prediction models for postoperative complications. It is, however, unclear if machine learning is superior to logistic regression when using structured clinical data. Postoperative pancreatic fistula and delayed gastric emptying are the two most common complications with the biggest impact on patient condition and length of hospital stay after pancreatoduodenectomy. This study aimed to compare the performance of machine learning and logistic regression in predicting pancreatic fistula and delayed gastric emptying after pancreatoduodenectomy. METHODS This retrospective observational study used nationwide data from 16 centers in the Dutch Pancreatic Cancer Audit between January 2014 and January 2021. The area under the curve of a machine learning and logistic regression model for clinically relevant postoperative pancreatic fistula and delayed gastric emptying were compared. RESULTS Overall, 799 (16.3%) patients developed a postoperative pancreatic fistula, and 943 developed (19.2%) delayed gastric emptying. For postoperative pancreatic fistula, the area under the curve of the machine learning model was 0.74, and the area under the curve of the logistic regression model was 0.73. For delayed gastric emptying, the area under the curve of the machine learning model and logistic regression was 0.59. CONCLUSION Machine learning did not outperform logistic regression modeling in predicting postoperative complications after pancreatoduodenectomy.
Collapse
Affiliation(s)
- Erik W Ingwersen
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands
| | - Wessel T Stam
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands
| | - Bono J V Meijs
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands
| | - Joran Roor
- SAS institute B.V., Huizen, the Netherlands
| | - Marc G Besselink
- Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands; Department of Surgery, Amsterdam UMC, University of Amsterdam, the Netherlands
| | | | | | | | | | - Freek Daams
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands.
| |
Collapse
|
22
|
Matsuo K, Aihara H, Hara Y, Morishita A, Sakagami Y, Miyake S, Tatsumi S, Ishihara S, Tohma Y, Yamashita H, Sasayama T. Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multi-Center Study for Development and Validation. J Neurotrauma 2023; 40:1694-1706. [PMID: 37029810 DOI: 10.1089/neu.2022.0515] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2023] Open
Abstract
The difficulty of accurately identifying patients who would benefit from promising treatments makes it challenging to prove the efficacy of novel treatments for traumatic brain injury (TBI). Although machine learning is being increasingly applied to this task, existing binary outcome prediction models are insufficient for the effective stratification of TBI patients. The aim of this study was to develop an accurate 3-class outcome prediction model to enable appropriate patient stratification. To this end, retrospective balanced data of 1200 blunt TBI patients admitted to six Japanese hospitals from January 2018 onwards (200 consecutive cases at each institution) were used for model training and validation. We incorporated 21 predictors obtained in the emergency department, including age, sex, six clinical findings, four laboratory parameters, eight computed tomography findings, and an emergency craniotomy. We developed two machine learning models (XGBoost and dense neural network) and logistic regression models to predict 3-class outcomes based on the Glasgow Outcome Scale-Extended (GOSE) at discharge. The prediction models were developed using a training dataset with n = 1000, and their prediction performances were evaluated over two validation rounds on a validation dataset (n = 80) and a test dataset (n = 120) using the bootstrap method. Of the 1200 patients in aggregate, the median patient age was 71 years, 199 (16.7%) exhibited severe TBI, and emergency craniotomy was performed on 104 patients (8.7%). The median length of stay was 13.0 days. The 3-class outcomes were good recovery/moderate disability for 709 patients (59.1%), severe disability/vegetative state in 416 patients (34.7%), and death in 75 patients (6.2%). XGBoost model performed well with 69.5% sensitivity, 82.5% accuracy, and an area under the receiver operating characteristic curve of 0.901 in the final validation. In terms of the receiver operating characteristic curve analysis, the XGBoost outperformed the neural network-based and logistic regression models slightly. In particular, XGBoost outperformed the logistic regression model significantly in predicting severe disability/vegetative state. Although each model predicted favorable outcomes accurately, they tended to miss the mortality prediction. The proposed machine learning model was demonstrated to be capable of accurate prediction of in-hospital outcomes following TBI, even with the three GOSE-based categories. As a result, it is expected to be more impactful in the development of appropriate patient stratification methods in future TBI studies than conventional binary prognostic models. Further, outcomes were predicted based on only clinical data obtained from the emergency department. However, developing a robust model with consistent performance in diverse scenarios remains challenging, and further efforts are needed to improve generalization performance.
Collapse
Affiliation(s)
- Kazuya Matsuo
- Department of Neurosurgery, Hyogo Emergency Medical Center and Kobe Red Cross Hospital, Kobe, Japan
- Department of Neurosurgery, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Hideo Aihara
- Department of Neurosurgery, Hyogo Prefectural Himeji Cardiovascular Center, Himeji, Japan
| | - Yoshie Hara
- Department of Neurosurgery, Hyogo Emergency Medical Center and Kobe Red Cross Hospital, Kobe, Japan
| | - Akitsugu Morishita
- Department of Neurosurgery, Hyogo Prefectural Kakogawa Medical Center, Kakogawa, Japan
| | - Yoshio Sakagami
- Department of Neurosurgery, Hyogo Prefectural Awaji Medical Center, Sumoto, Japan
| | - Shigeru Miyake
- Department of Neurosurgery, Kita-harima Medical Center, Ono, Japan
| | - Shotaro Tatsumi
- Department of Neurosurgery, Hirohata Steel Memorial Hospital, Himeji, Japan
| | - Satoshi Ishihara
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Kobe, Japan
| | - Yoshiki Tohma
- Acute Care Medical Center, Hyogo Prefectural Kakogawa Medical Center, Kakogawa, Japan
| | - Haruo Yamashita
- Department of Neurosurgery, Hyogo Emergency Medical Center and Kobe Red Cross Hospital, Kobe, Japan
| | - Takashi Sasayama
- Department of Neurosurgery, Kobe University Graduate School of Medicine, Kobe, Japan
| |
Collapse
|
23
|
Wang J, Yin MJ, Wen HC. Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2023; 23:142. [PMID: 37507752 PMCID: PMC10385965 DOI: 10.1186/s12911-023-02247-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/25/2023] [Indexed: 07/30/2023] Open
Abstract
PURPOSE With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI. METHODOLOGY We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity. RESULT A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance. CONCLUSION According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.
Collapse
Affiliation(s)
- Jue Wang
- Department of Emergency, The First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China
| | - Ming Jing Yin
- Department of Emergency, The First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China
| | - Han Chun Wen
- Department of Emergency, The First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China.
- Intensive Care Department, Guangxi Medical University First Affiliated Hospital, Ward 1, No. 6 Shuangyong Road, Qingxiu District, Guangxi Zhuang Autonomous Region, Nanning, China.
| |
Collapse
|
24
|
Courville E, Kazim SF, Vellek J, Tarawneh O, Stack J, Roster K, Roy J, Schmidt M, Bowers C. Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis. Surg Neurol Int 2023; 14:262. [PMID: 37560584 PMCID: PMC10408617 DOI: 10.25259/sni_312_2023] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/21/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI management. This study aimed to identify ML models with demonstrated effectiveness in predicting TBI outcomes. METHODS We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. In total, 15 articles were identified using the search strategy. Patient demographics, clinical status, ML outcome variables, and predictive characteristics were extracted. A small meta-analysis of mortality prediction was performed, and a meta-analysis of diagnostic accuracy was conducted for ML algorithms used across multiple studies. RESULTS ML algorithms including support vector machine (SVM), artificial neural networks (ANN), random forest, and Naïve Bayes were compared to logistic regression (LR). Thirteen studies found significant improvement in prognostic capability using ML versus LR. The accuracy of the above algorithms was consistently over 80% when predicting mortality and unfavorable outcome measured by Glasgow Outcome Scale. Receiver operating characteristic curves analyzing the sensitivity of ANN, SVM, decision tree, and LR demonstrated consistent findings across studies. Lower admission Glasgow Coma Scale (GCS), older age, elevated serum acid, and abnormal glucose were associated with increased adverse outcomes and had the most significant impact on ML algorithms. CONCLUSION ML algorithms were stronger than traditional regression models in predicting adverse outcomes. Admission GCS, age, and serum metabolites all have strong predictive power when used with ML and should be considered important components of TBI risk stratification.
Collapse
Affiliation(s)
- Evan Courville
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, United States
| | - Syed Faraz Kazim
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, United States
| | - John Vellek
- Department of Neurosurgery, School of Medicine, New York Medical College, Valhalla, New York, United States
| | - Omar Tarawneh
- Department of Neurosurgery, School of Medicine, New York Medical College, Valhalla, New York, United States
| | - Julia Stack
- Department of Neurosurgery, School of Medicine, New York Medical College, Valhalla, New York, United States
| | - Katie Roster
- Department of Neurosurgery, School of Medicine, New York Medical College, Valhalla, New York, United States
| | - Joanna Roy
- Department of Neurosurgery, Topiwala National Medical and B. Y. L. Nair Charitable Hospital, Mumbai, Maharashtra, India
| | - Meic Schmidt
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, United States
| | - Christian Bowers
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, United States
| |
Collapse
|
25
|
Siqueira Pinto M, Winzeck S, Kornaropoulos EN, Richter S, Paolella R, Correia MM, Glocker B, Williams G, Vik A, Posti JP, Haberg A, Stenberg J, Guns PJ, den Dekker AJ, Menon DK, Sijbers J, Van Dyck P, Newcombe VFJ. Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study. J Neurotrauma 2023; 40:1317-1338. [PMID: 36974359 DOI: 10.1089/neu.2022.0365] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to improve outcome prediction. In this study, we explore the potential of linear support vector classifiers (linearSVCs) to identify dMRI biomarkers that can predict recovery after mTBI. Simultaneously, the harmonization of fractional anisotropy (FA) and mean diffusivity (MD) via ComBat was evaluated and compared for the classification performances of the linearSVCs. We included dMRI scans of 179 mTBI patients and 85 controls from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), a multi-center prospective cohort study, up to 21 days post-injury. Patients were dichotomized according to their Extended Glasgow Outcome Scale (GOSE) scores at 6 months into complete (n = 92; GOSE = 8) and incomplete (n = 87; GOSE <8) recovery. FA and MD maps were registered to a common space and harmonized via the ComBat algorithm. LinearSVCs were applied to distinguish: (1) mTBI patients from controls and (2) mTBI patients with complete from those with incomplete recovery. The linearSVCs were trained on (1) age and sex only, (2) non-harmonized, (3) two-category-harmonized ComBat, and (4) three-category-harmonized ComBat FA and MD images combined with age and sex. White matter FA and MD voxels and regions of interest (ROIs) within the John Hopkins University (JHU) atlas were examined. Recursive feature elimination was used to identify the 10% most discriminative voxels or the 10 most discriminative ROIs for each implementation. mTBI patients displayed significantly higher MD and lower FA values than controls for the discriminative voxels and ROIs. For the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD voxel-wise linearSVC provided significantly higher classification scores (81.4% accuracy, 93.3% sensitivity, 80.3% F1-score, and 0.88 area under the curve [AUC], p < 0.05) compared with the classification based on age and sex only and the ROI approaches (accuracies: 59.8% and 64.8%, respectively). Similar to the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD maps voxelwise approach yields statistically significant prediction scores between mTBI patients with complete and those with incomplete recovery (71.8% specificity, 66.2% F1-score and 0.71 AUC, p < 0.05), which provided a modest increase in the classification score (accuracy: 66.4%) compared with the classification based on age and sex only and ROI-wise approaches (accuracy: 61.4% and 64.7%, respectively). This study showed that ComBat harmonized FA and MD may provide additional information for diagnosis and prognosis of mTBI in a multi-modal machine learning approach. These findings demonstrate that dMRI may assist in the early detection of patients at risk of incomplete recovery from mTBI.
Collapse
Affiliation(s)
- Maíra Siqueira Pinto
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Stefan Winzeck
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Evgenios N Kornaropoulos
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Sophie Richter
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Roberto Paolella
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
- Icometrix, Leuven, Belgium
| | - Marta M Correia
- MRC Cognition and Brain Sciences Unit, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Ben Glocker
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Guy Williams
- Wolfson Brain Imaging Centre, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Anne Vik
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jussi P Posti
- Department of Neurosurgery and Turku Brain Injury Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Asta Haberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jonas Stenberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | | | - Arnold J den Dekker
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Jan Sijbers
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- mVISION, University of Antwerp, Antwerp, Belgium
| | - Virginia F J Newcombe
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
26
|
Rajaei F, Cheng S, Williamson CA, Wittrup E, Najarian K. AI-Based Decision Support System for Traumatic Brain Injury: A Survey. Diagnostics (Basel) 2023; 13:diagnostics13091640. [PMID: 37175031 PMCID: PMC10177859 DOI: 10.3390/diagnostics13091640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/22/2023] [Accepted: 04/29/2023] [Indexed: 05/15/2023] Open
Abstract
Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.
Collapse
Affiliation(s)
- Flora Rajaei
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Shuyang Cheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Craig A Williamson
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emily Wittrup
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Data-Driven Drug Development and Treatment Assessment (DATA), University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
27
|
Loeffler SE, Trayanova N. Primer on Machine Learning in Electrophysiology. Arrhythm Electrophysiol Rev 2023; 12:e06. [PMID: 37427298 PMCID: PMC10323871 DOI: 10.15420/aer.2022.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/10/2023] [Indexed: 07/11/2023] Open
Abstract
Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.
Collapse
Affiliation(s)
- Shane E Loeffler
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University Baltimore, MD, US
| | - Natalia Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University Baltimore, MD, US
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US
| |
Collapse
|
28
|
An efficient edge/cloud medical system for rapid detection of level of consciousness in emergency medicine based on explainable machine learning models. Neural Comput Appl 2023; 35:10695-10716. [PMID: 37155550 PMCID: PMC10015549 DOI: 10.1007/s00521-023-08258-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 01/06/2023] [Indexed: 03/17/2023]
Abstract
Emergency medicine (EM) is one of the attractive research fields in which researchers investigate their efforts to diagnose and treat unforeseen illnesses or injuries. There are many tests and observations are involved in EM. Detection of the level of consciousness is one of these observations, which can be detected using several methods. Among these methods, the automatic estimation of the Glasgow coma scale (GCS) is studied in this paper. The GCS is a medical score used to describe a patient’s level of consciousness. This type of scoring system requires medical examination that may not be available with the shortage of the medical expert. Therefore, the automatic medical calculation for a patient’s level of consciousness is highly needed. Artificial intelligence has been deployed in several applications and appears to have a high performance regarding providing automatic solutions. The main objective of this work is to introduce the edge/cloud system to improve the efficiency of the consciousness measurement through efficient local data processing. Moreover, an efficient machine learning (ML) model to predict the level of consciousness of a certain patient based on the patient’s demographic, vital signs, and laboratory tests is proposed, as well as maintaining the explainability issue using Shapley additive explanations (SHAP) that provides natural language explanation in a form that helps the medical expert to understand the final prediction. The developed ML model is validated using vital signs and laboratory tests extracted from the MIMIC III dataset, and it achieves superior performance (mean absolute error (MAE) = 0.269, mean square error (MSE) = 0.625, R2 score = 0.964). The resulting model is accurate, medically intuitive, and trustworthy.
Collapse
|
29
|
Song J, Shin SD, Jamaluddin SF, Chiang WC, Tanaka H, Song KJ, Ahn S, Park JH, Kim J, Cho HJ, Moon S, Jeon ET. Prediction of Mortality among Patients with Isolated Traumatic Brain Injury Using Machine Learning Models in Asian Countries: An International Multi-Center Cohort Study. J Neurotrauma 2023. [PMID: 36656672 DOI: 10.1089/neu.2022.0280] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. The Pan-Asian Trauma Outcome Study registry was used in this study, and the data were prospectively collected from January 1, 2015, to December 31, 2020. Among a total of 6540 patients (≥ 15 years) with isolated moderate and severe TBI, 3276 (50.1%) patients were randomly included with stratification by outcomes and subgrouping variables for model evaluation, and 3264 (49.9%) patients were included for model training and validation. Logistic regression was considered as a baseline, and ML models were constructed and evaluated using the area under the precision-recall curve (AUPRC) as the primary outcome metric, area under the receiver operating characteristic curve (AUROC), and precision at fixed levels of recall. The contribution of the variables to the model prediction was measured using the SHapley Additive exPlanations (SHAP) method. The ML models outperformed logistic regression in predicting the in-hospital mortality. Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O2 saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.
Collapse
Affiliation(s)
- Juhyun Song
- Department of Emergency Medicine, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Sang Do Shin
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | | | - Wen-Chu Chiang
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei City, Taiwan
| | - Hideharu Tanaka
- Graduate School of Emergency Medical Service System, Kokushikan University, Tokyo, Japan
| | - Kyoung Jun Song
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sejoong Ahn
- Department of Emergency Medicine, Korea University Ansan Hospital, Ansan-si, Republic of Korea
| | - Jong-Hak Park
- Department of Emergency Medicine, Korea University Ansan Hospital, Ansan-si, Republic of Korea
| | - Jooyeong Kim
- Department of Emergency Medicine, Korea University Ansan Hospital, Ansan-si, Republic of Korea
| | - Han-Jin Cho
- Department of Emergency Medicine, Korea University Ansan Hospital, Ansan-si, Republic of Korea
| | - Sungwoo Moon
- Department of Emergency Medicine, Korea University Ansan Hospital, Ansan-si, Republic of Korea
| | - Eun-Tae Jeon
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| |
Collapse
|
30
|
Kn BP, Cs A, Mohammed A, Chitta KK, To XV, Srour H, Nasrallah F. An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury. Med Biol Eng Comput 2023; 61:847-865. [PMID: 36624356 DOI: 10.1007/s11517-022-02752-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 12/24/2022] [Indexed: 01/11/2023]
Abstract
Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra-areas of secondary neural injury which are crucial targets for therapeutic interventions. Segmenting manually areas of ongoing changes like necrosis, edema, hematoma, and inflammation is tedious, error-prone, and biased. Using the multi-parametric MR data from a rodent model study, we demonstrate the effectiveness of an end-end deep learning global-attention-based UNet (GA-UNet) framework for automatic segmentation and quantification of TBI lesions. Longitudinal MR scans (2 h, 1, 3, 7, 14, 30, and 60 days) were performed on eight Sprague-Dawley rats after controlled cortical injury was performed. TBI lesion and sub-regions segmentation was performed using 3D-UNet and GA-UNet. Dice statistics (DSI) and Hausdorff distance were calculated to assess the performance. MR scan variations-based (bias, noise, blur, ghosting) data augmentation was performed to develop a robust model.Training/validation median DSI for U-Net was 0.9368 with T2w and MPRAGE inputs, whereas GA-UNet had 0.9537 for the same. Testing accuracies were higher for GA-UNet than U-Net with a DSI of 0.8232 for the T2w-MPRAGE inputs.Longitudinally, necrosis remained constant while oligemia and penumbra decreased, and edema appearing around day 3 which increased with time. GA-UNet shows promise for multi-contrast MR image-based segmentation/quantification of TBI in large cohort studies.
Collapse
Affiliation(s)
- Bhanu Prakash Kn
- Clinical Data Analytics & Radiomics, Cellular Image Informatics, Bioinformatics Institute, A*STAR, 30 Biopolis St Matrix, Singapore, 138671, Singapore. .,Cellular Image Informatics, Bioinformatics Institute, A*STAR Horizontal Technology Centers, Singapore, Singapore.
| | - Arvind Cs
- Clinical Data Analytics & Radiomics, Cellular Image Informatics, Bioinformatics Institute, A*STAR, 30 Biopolis St Matrix, Singapore, 138671, Singapore
| | - Abdalla Mohammed
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Krishna Kanth Chitta
- Signal and Image Processing Group, Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, A*STAR, 02-02 Helios 11, Biopolis Way, Singapore, 138667, Singapore
| | - Xuan Vinh To
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Hussein Srour
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Fatima Nasrallah
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| |
Collapse
|
31
|
Wu T, Wei Y, Wu J, Yi B, Li H. Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome. Sci Rep 2023; 13:2485. [PMID: 36774378 PMCID: PMC9922285 DOI: 10.1038/s41598-023-28421-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 01/18/2023] [Indexed: 02/13/2023] Open
Abstract
To evaluate the performance of machine learning (ML) models and to compare it with logistic regression (LR) technique in predicting cognitive impairment related to post intensive care syndrome (PICS-CI). We conducted a prospective observational study of ICU patients at two tertiary hospitals. A cohort of 2079 patients was screened, and finally 481 patients were included. Seven different ML models were considered, decision tree (DT), random forest (RF), XGBoost, neural network (NN), naïve bayes (NB), and support vector machine (SVM), and compared with logistic regression (LR). Discriminative ability was evaluated by area under the receiver operating characteristic curve (AUC), calibration belt plots, and Hosmer-Lemeshow test was used to assess calibration. Decision curve analysis was performed to quantify clinical utility. Duration of delirium, poor Richards-Campbell sleep questionnaire (RCSQ) score, advanced age, and sepsis were the most frequent and important candidates risk factors for PICS-CI. All ML models showed good performance (AUC range: 0.822-0.906). NN model had the highest AUC (0.906 [95% CI 0.857-0.955]), which was slightly higher than, but not significantly different from that of LR (0.898 [95% CI 0.847-0.949]) (P > 0.05, Delong test). Given the overfitting and complexity of some ML models, the LR model was then used to develop a web-based risk calculator to aid decision-making ( https://model871010.shinyapps.io/dynnomapp/ ). In a low dimensional data, LR may yield as good performance as other complex ML models to predict cognitive impairment after ICU hospitalization.
Collapse
Affiliation(s)
- TingTing Wu
- The School of Nursing, Fujian Medical University, Fujian, China
- Research Center for Nursing Theory and Practice, Fujian Provincial Hospital, No 134, East Street, Gulou District, Fuzhou, 35001, Fujian, China
| | - YueQing Wei
- Research Center for Nursing Theory and Practice, Fujian Provincial Hospital, No 134, East Street, Gulou District, Fuzhou, 35001, Fujian, China
- Respiratory and Intensive Care Unit, Fujian Provincial Hospital, Fujian, China
| | - JingBing Wu
- Research Center for Nursing Theory and Practice, Fujian Provincial Hospital, No 134, East Street, Gulou District, Fuzhou, 35001, Fujian, China
- Medical Intensive Care Unit, Fujian Provincial Hospital, Fujian, China
| | - BiLan Yi
- Research Center for Nursing Theory and Practice, Fujian Provincial Hospital, No 134, East Street, Gulou District, Fuzhou, 35001, Fujian, China
- Respiratory and Intensive Care Unit, Fujian Provincial Hospital, Fujian, China
| | - Hong Li
- The School of Nursing, Fujian Medical University, Fujian, China.
- Research Center for Nursing Theory and Practice, Fujian Provincial Hospital, No 134, East Street, Gulou District, Fuzhou, 35001, Fujian, China.
| |
Collapse
|
32
|
Khalili H, Rismani M, Nematollahi MA, Masoudi MS, Asadollahi A, Taheri R, Pourmontaseri H, Valibeygi A, Roshanzamir M, Alizadehsani R, Niakan A, Andishgar A, Islam SMS, Acharya UR. Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci Rep 2023; 13:960. [PMID: 36653412 PMCID: PMC9849475 DOI: 10.1038/s41598-023-28188-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.
Collapse
Affiliation(s)
- Hosseinali Khalili
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maziyar Rismani
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | | | - Mohammad Sadegh Masoudi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arefeh Asadollahi
- Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Reza Taheri
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Hossein Pourmontaseri
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran.,Bitab Knowledge Enterprise, Fasa University of Medical Sciences, Fasa, Iran
| | - Adib Valibeygi
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Amin Niakan
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Aref Andishgar
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia.,Cardiovascular Division, The George Institute for Global Health, Newtown, Australia.,Sydney Medical School, University of Sydney, Camperdown, Australia
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan
| |
Collapse
|
33
|
Vedaei F, Mashhadi N, Zabrecky G, Monti D, Navarreto E, Hriso C, Wintering N, Newberg AB, Mohamed FB. Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques. Front Neurosci 2023; 16:1099560. [PMID: 36699521 PMCID: PMC9869678 DOI: 10.3389/fnins.2022.1099560] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration [clinicaltrials.gov], identifier [NCT03241732].
Collapse
Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Najmeh Mashhadi
- Department of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, United States
| | - George Zabrecky
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel Monti
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Andrew B. Newberg
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| |
Collapse
|
34
|
Lin C, Pan LF, He ZQ, Hsu S. Early prediction of 30- and 14-day all-cause unplanned readmissions. Health Informatics J 2023; 29:14604582231164694. [PMID: 36913624 DOI: 10.1177/14604582231164694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
BACKGROUND An unplanned readmission is a dual metric for both the cost and quality of medical care. METHODS We employed the random forest (RF) method to build a prediction model using a large dataset from patients' electronic health records (EHRs) from a medical center in Taiwan. The discrimination abilities between the RF and regression-based models were compared using the areas under the ROC curves (AUROC). RESULTS When compared with standardized risk prediction tools, the RF constructed using data readily available at admission had a marginally yet significantly better ability to identify high-risk readmissions within 30 and 14 days without compromising sensitivity and specificity. The most important predictor for 30-day readmissions was directly related to the representing factors of index hospitalization, whereas for 14-day readmissions the most important predictor was associated with a higher chronic illness burden. CONCLUSIONS Identifying dominant risk factors based on index admission and different readmission time intervals is crucial for healthcare planning.
Collapse
Affiliation(s)
- Chaohsin Lin
- Department of Risk Management and Insurance, 517768National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Li-Fei Pan
- Department of General Affairs Administration, 38024Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Zuo-Quan He
- Department of Risk Management and Insurance, 517768National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Shuofen Hsu
- Department of Risk Management and Insurance, 517768National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| |
Collapse
|
35
|
Ramasubramanian B, Reddy VS, Chellappan V, Ramakrishna S. Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases. BIOSENSORS 2022; 12:1176. [PMID: 36551143 PMCID: PMC9775999 DOI: 10.3390/bios12121176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn't been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases.
Collapse
Affiliation(s)
- Brindha Ramasubramanian
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Vundrala Sumedha Reddy
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
| | - Vijila Chellappan
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Seeram Ramakrishna
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
| |
Collapse
|
36
|
Poskart B, Iskierka G, Krot K, Burduk R, Gwizdal P, Gola A. Multi-Parameter Predictive Model of Mobile Robot's Battery Discharge for Intelligent Mission Planning in Multi-Robot Systems. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249861. [PMID: 36560229 PMCID: PMC9786877 DOI: 10.3390/s22249861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 06/12/2023]
Abstract
The commercially available battery management and mission scheduling systems for fleets of autonomous mobile robots use different algorithms to calculate the current state of charge of the robot's battery. This information alone cannot be used to predict whether it will be possible for a single robot in the fleet to execute all of the scheduled missions. This paper provides insight into how to develop a universal battery discharge model based on key mission parameters, which allows for predicting the battery usage over the course of the scheduled missions and can, in turn, be used to determine which missions to delegate to other robots in the fleet, or if more robots are needed in the fleet to accomplish the production plan. The resulting model is, therefore, necessary for mission scheduling in a flexible production system, including autonomous mobile robot transportation networks.
Collapse
Affiliation(s)
- Bartosz Poskart
- Faculty of Mechanical Engineering, Wrocław University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, Poland
| | - Grzegorz Iskierka
- Faculty of Mechanical Engineering, Wrocław University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, Poland
| | - Kamil Krot
- Faculty of Mechanical Engineering, Wrocław University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, Poland
| | - Robert Burduk
- Faculty of Information and Communication Technology, Wrocław University of Science and Technology, ul. Z. Janiszewskiego 7, 50-372 Wrocław, Poland
| | - Paweł Gwizdal
- Faculty of Mechanical Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20-618 Lublin, Poland
| | - Arkadiusz Gola
- Faculty of Mechanical Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20-618 Lublin, Poland
| |
Collapse
|
37
|
Puybasset L, Perlbarg V, Unrug J, Cassereau D, Galanaud D, Torkomian G, Battisti V, Lefort M, Velly L, Degos V, Citerio G, Bayen É, Pelegrini-Issac M. Prognostic value of global deep white matter DTI metrics for 1-year outcome prediction in ICU traumatic brain injury patients: an MRI-COMA and CENTER-TBI combined study. Intensive Care Med 2022; 48:201-212. [PMID: 34904191 DOI: 10.1007/s00134-021-06583-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/11/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE A reliable tool for outcome prognostication in severe traumatic brain injury (TBI) would improve intensive care unit (ICU) decision-making process by providing objective information to caregivers and family. This study aimed at designing a new classification score based on magnetic resonance (MR) diffusion metrics measured in the deep white matter between day 7 and day 35 after TBI to predict 1-year clinical outcome. METHODS Two multicenter cohorts (29 centers) were used. MRI-COMA cohort (NCT00577954) was split into MRI-COMA-Train (50 patients enrolled between 2006 and mid-2014) and MRI-COMA-Test (140 patients followed up in clinical routine from 2014) sub-cohorts. These latter patients were pooled with 56 ICU patients (enrolled from 2014 to 2020) from CENTER-TBI cohort (NCT02210221). Patients were dichotomised depending on their 1-year Glasgow outcome scale extended (GOSE) score: GOSE 1-3, unfavorable outcome (UFO); GOSE 4-8, favorable outcome (FO). A support vector classifier incorporating fractional anisotropy and mean diffusivity measured in deep white matter, and age at the time of injury was developed to predict whether the patients would be either UFO or FO. RESULTS The model achieved an area under the ROC curve of 0.93 on MRI-COMA-Train training dataset, and 49% sensitivity for 96.8% specificity in predicting UFO and 58.5% sensitivity for 97.1% specificity in predicting FO on the pooled MRI-COMA-Test and CENTER-TBI validation datasets. CONCLUSION The model successfully identified, with a specificity compatible with a personalized decision-making process in ICU, one in two patients who had an unfavorable outcome at 1 year after the injury, and two-thirds of the patients who experienced a favorable outcome.
Collapse
Affiliation(s)
- Louis Puybasset
- Neurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France.
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France.
- Department of Anesthesiology and Intensive Care, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
- Clinical Research Group 29, Sorbonne Université, Paris, France.
| | | | - Jean Unrug
- Neurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
| | - Didier Cassereau
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
| | - Damien Galanaud
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
- Department of Neuroradiology, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Grégory Torkomian
- Neurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Valentine Battisti
- Neurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Muriel Lefort
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
| | - Lionel Velly
- Department of Anesthesiology and Critical Care Medicine, University Hospital Timone, AP-HM, Aix Marseille University, Marseille, France
- CNRS, Institute of Neuroscience Timone, UMR7289, Aix Marseille University, Marseille, France
| | - Vincent Degos
- Clinical Research Group 29, Sorbonne Université, Paris, France
- Department of Anesthesia, Critical Care and Peri-Operative Medicine, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
- INSERM UMR 1141, Paris, France
| | - Guiseppe Citerio
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- Neurointensive Care Unit, Department of Emergency and Urgency, ASST-Monza, San Gerardo Hospital, Monza, Italy
| | - Éléonore Bayen
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
- Rehabilitation Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | | |
Collapse
|
38
|
Minoccheri C, Williamson CA, Hemmila M, Ward K, Stein EB, Gryak J, Najarian K. An interpretable neural network for outcome prediction in traumatic brain injury. BMC Med Inform Decis Mak 2022; 22:203. [PMID: 35915430 PMCID: PMC9341077 DOI: 10.1186/s12911-022-01953-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 07/21/2022] [Indexed: 11/30/2022] Open
Abstract
Background Traumatic Brain Injury (TBI) is a common condition with potentially severe long-term complications, the prediction of which remains challenging. Machine learning (ML) methods have been used previously to help physicians predict long-term outcomes of TBI so that appropriate treatment plans can be adopted. However, many ML techniques are “black box”: it is difficult for humans to understand the decisions made by the model, with post-hoc explanations only identifying isolated relevant factors rather than combinations of factors. Moreover, such models often rely on many variables, some of which might not be available at the time of hospitalization. Methods In this study, we apply an interpretable neural network model based on tropical geometry to predict unfavorable outcomes at six months from hospitalization in TBI patients, based on information available at the time of admission. Results The proposed method is compared to established machine learning methods—XGBoost, Random Forest, and SVM—achieving comparable performance in terms of area under the receiver operating characteristic curve (AUC)—0.799 for the proposed method vs. 0.810 for the best black box model. Moreover, the proposed method allows for the extraction of simple, human-understandable rules that explain the model’s predictions and can be used as general guidelines by clinicians to inform treatment decisions. Conclusions The classification results for the proposed model are comparable with those of traditional ML methods. However, our model is interpretable, and it allows the extraction of intelligible rules. These rules can be used to determine relevant factors in assessing TBI outcomes and can be used in situations when not all necessary factors are known to inform the full model’s decision.
Collapse
|
39
|
de Hond AAH, Kant IMJ, Honkoop PJ, Smith AD, Steyerberg EW, Sont JK. Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations. Sci Rep 2022; 12:20363. [PMID: 36437306 PMCID: PMC9701686 DOI: 10.1038/s41598-022-24909-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 11/22/2022] [Indexed: 11/28/2022] Open
Abstract
Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82-0.87) and 0.88 (0.86-0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity.
Collapse
Affiliation(s)
- Anne A. H. de Hond
- grid.10419.3d0000000089452978Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, The Netherlands ,grid.10419.3d0000000089452978Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, The Netherlands ,grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, the Netherlands
| | - Ilse M. J. Kant
- grid.10419.3d0000000089452978Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, The Netherlands ,grid.10419.3d0000000089452978Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, The Netherlands ,grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, the Netherlands
| | - Persijn J. Honkoop
- grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, the Netherlands
| | - Andrew D. Smith
- grid.417145.20000 0004 0624 9990Department of Respiratory Medicine, University Hospital Wishaw, 50 Netherton Street, Wishaw, ML2 0DP UK
| | - Ewout W. Steyerberg
- grid.10419.3d0000000089452978Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, The Netherlands ,grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, the Netherlands
| | - Jacob K. Sont
- grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, the Netherlands
| |
Collapse
|
40
|
Pruneski JA, Pareek A, Kunze KN, Martin RK, Karlsson J, Oeding JF, Kiapour AM, Nwachukwu BU, Williams RJ. Supervised machine learning and associated algorithms: applications in orthopedic surgery. Knee Surg Sports Traumatol Arthrosc 2022; 31:1196-1202. [PMID: 36222893 DOI: 10.1007/s00167-022-07181-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 09/20/2022] [Indexed: 11/07/2022]
Abstract
Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.
Collapse
Affiliation(s)
- James A Pruneski
- Department of Orthopedic Surgery, Boston Children's Hospital, Boston, MA, USA
| | - Ayoosh Pareek
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
| | - Kyle N Kunze
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - R Kyle Martin
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jón Karlsson
- Orthopaedic Research Department, Göteborg University, Göteborg, Sweden
| | - Jacob F Oeding
- School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN, USA
| | - Ata M Kiapour
- Department of Orthopedic Surgery, Boston Children's Hospital, Boston, MA, USA
| | - Benedict U Nwachukwu
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Riley J Williams
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| |
Collapse
|
41
|
Stoitsas K, Bahulikar S, de Munter L, de Jongh MAC, Jansen MAC, Jung MM, van Wingerden M, Van Deun K. Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery. Sci Rep 2022; 12:16990. [PMID: 36216874 PMCID: PMC9550811 DOI: 10.1038/s41598-022-21390-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 09/27/2022] [Indexed: 12/29/2022] Open
Abstract
Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised techniques are employed to cluster patients based on longitudinal recovery profiles. Subsequently, these data-driven clusters were assessed on clinical validity by experts and used as targets in supervised machine learning models. We present a formalised analysis of the obtained clusters that incorporates evaluation of (i) statistical and machine learning metrics, (ii) clusters clinical validity with descriptive statistics and medical expertise. Clusters quality assessment revealed that clusters obtained through a Bayesian method (High Dimensional Supervised Classification and Clustering) and a Deep Gaussian Mixture model, in combination with oversampling and a Random Forest for supervised learning of the cluster assignments provided among the most clinically sensible partitioning of patients. Other methods that obtained higher classification accuracy suffered from cluster solutions with large majority classes or clinically less sensible classes. Models that used just physical or a mix of physical and psychological outcomes proved to be among the most sensible, suggesting that clustering on psychological outcomes alone yields recovery profiles that do not conform to known risk factors.
Collapse
Affiliation(s)
- Kostas Stoitsas
- Department of Methodology and Statistics, Tilburg University, Tilburg, 5000 LE, The Netherlands.
| | - Saurabh Bahulikar
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, 5000 LE, The Netherlands
| | - Leonie de Munter
- Department Traumatology, ETZ Hospital, Hilvarenbeekseweg 60, 5022 GC, Tilburg, The Netherlands
| | - Mariska A C de Jongh
- Network Emergency Care Brabant, Brabant Trauma Registry, Hilvarenbeekseweg 60, 5022 GC, Tilburg, The Netherlands
| | - Maria A C Jansen
- Network Emergency Care Brabant, Brabant Trauma Registry, Hilvarenbeekseweg 60, 5022 GC, Tilburg, The Netherlands
| | - Merel M Jung
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, 5000 LE, The Netherlands
| | - Marijn van Wingerden
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, 5000 LE, The Netherlands
| | - Katrijn Van Deun
- Department of Methodology and Statistics, Tilburg University, Tilburg, 5000 LE, The Netherlands
| |
Collapse
|
42
|
Alouani AT, Elfouly T. Traumatic Brain Injury (TBI) Detection: Past, Present, and Future. Biomedicines 2022; 10:biomedicines10102472. [PMID: 36289734 PMCID: PMC9598576 DOI: 10.3390/biomedicines10102472] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
Traumatic brain injury (TBI) can produce temporary biochemical imbalance due to leaks through cell membranes or disruption of the axoplasmic flow due to the misalignment of intracellular neurofilaments. If untreated, TBI can lead to Alzheimer's, Parkinson's, or total disability. Mild TBI (mTBI) accounts for about about 90 percent of all TBI cases. The detection of TBI as soon as it happens is crucial for successful treatment management. Neuroimaging-based tests provide only a structural and functional mapping of the brain with poor temporal resolution. Such tests may not detect mTBI. On the other hand, the electroencephalogram (EEG) provides good spatial resolution and excellent temporal resolution of the brain activities beside its portability and low cost. The objective of this paper is to provide clinicians and scientists with a one-stop source of information to quickly learn about the different technologies used for TBI detection, their advantages and limitations. Our research led us to conclude that even though EEG-based TBI detection is potentially a powerful technology, it is currently not able to detect the presence of a mTBI with high confidence. The focus of the paper is to review existing approaches and provide the reason for the unsuccessful state of EEG-based detection of mTBI.
Collapse
|
43
|
Lee SH, Lee CH, Hwang SH, Kang DH. A Machine Learning-Based Prognostic Model for the Prediction of Early Death After Traumatic Brain Injury: Comparison with the Corticosteroid Randomization After Significant Head Injury (CRASH) Model. World Neurosurg 2022; 166:e125-e134. [PMID: 35787963 DOI: 10.1016/j.wneu.2022.06.130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 06/24/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Machine learning (ML) has been used to predict the outcomes of traumatic brain injury. However, few studies have reported the use of ML models to predict early death. This study aimed to develop ML models for early death prediction and to compare performance with the corticosteroid randomization after significant head injury (CRASH) model. METHODS We retrospectively reviewed traumatic brain injury patients between February 2017 and August 2021. The patients were randomly assigned to a training set and a test set. Predictive variables included clinical findings, laboratory values, and computed tomography findings. The ML models (random forest, support vector machine [SVM], logistic regression) were developed with the training set. The CRASH model is a prognostic model that was developed based on 10,008 patients included in the CRASH trial. The ML and CRASH models were applied to the test set to evaluate the performance. RESULTS A total of 423 patients were included; 317 and 106 patients were randomly assigned to the training and test sets, respectively. The area under the curve was highest in the SVM (0.952, 95% confidence interval = 0.906-0.990) and lowest in the CRASH model (0.942, 95% confidence interval = 0.886-0.999). There were no significant differences between the area under the curves of the ML and CRASH models (P = 0.899 for random forest vs. the CRASH model, P = 0.760 for SVM vs. the CRASH model, P = 0.806 for logistic regression vs. the CRASH model). CONCLUSIONS The ML models may have comparable performances compared to the CRASH model despite being developed with a smaller sample size.
Collapse
Affiliation(s)
- Sang Hyub Lee
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Chul Hee Lee
- Department of Neurosurgery, Gyeongsang National University Hospital, Gyeongsang National University School of Medicine, Jinju-Si, Gyeongsangnam-do, Republic of Korea.
| | - Soo Hyun Hwang
- Department of Neurosurgery, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Seongsan-gu, Changwon-Si, Gyeongsangnam-do, Republic of Korea
| | - Dong Ho Kang
- Department of Neurosurgery, Gyeongsang National University Hospital, Gyeongsang National University School of Medicine, Jinju-Si, Gyeongsangnam-do, Republic of Korea
| |
Collapse
|
44
|
Mijderwijk HJ, Nieboer D, Incekara F, Berger K, Steyerberg EW, van den Bent MJ, Reifenberger G, Hänggi D, Smits M, Senft C, Rapp M, Sabel M, Voss M, Forster MT, Kamp MA. Development and external validation of a clinical prediction model for survival in patients with IDH wild-type glioblastoma. J Neurosurg 2022; 137:914-923. [PMID: 35171829 DOI: 10.3171/2021.10.jns211261] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 10/14/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Prognostication of glioblastoma survival has become more refined due to the molecular reclassification of these tumors into isocitrate dehydrogenase (IDH) wild-type and IDH mutant. Since this molecular stratification, however, robust clinical prediction models relevant to the entire IDH wild-type glioblastoma patient population are lacking. This study aimed to provide an updated model that predicts individual survival prognosis in patients with IDH wild-type glioblastoma. METHODS Databases from Germany and the Netherlands provided data on 1036 newly diagnosed glioblastoma patients treated between 2012 and 2018. A clinical prediction model for all-cause mortality was developed with Cox proportional hazards regression. This model included recent glioblastoma-associated molecular markers in addition to well-known classic prognostic variables, which were updated and refined with additional categories. Model performance was evaluated according to calibration (using calibration plots and calibration slope) and discrimination (using a C-statistic) in a cross-validation procedure by country to assess external validity. RESULTS The German and Dutch patient cohorts consisted of 710 and 326 patients, respectively, of whom 511 (72%) and 308 (95%) had died. Three models were developed, each with increasing complexity. The final model considering age, sex, preoperative Karnofsky Performance Status, extent of resection, O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and adjuvant therapeutic regimen showed an optimism-corrected C-statistic of 0.73 (95% confidence interval 0.71-0.75). Cross-validation between the national cohorts yielded comparable results. CONCLUSIONS This prediction model reliably predicts individual survival prognosis in patients with newly diagnosed IDH wild-type glioblastoma, although additional validation, especially for long-term survival, may be desired. The nomogram and web application of this model may support shared decision-making if used properly.
Collapse
Affiliation(s)
| | | | - Fatih Incekara
- 3Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam
- Departments of4Neurosurgery and
| | | | - Ewout W Steyerberg
- Departments of2Public Health and
- 5Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Martin J van den Bent
- 6Neurology, Brain Tumor Centre, Erasmus MC Cancer Institute, University Medical Center, Rotterdam
| | - Guido Reifenberger
- 7Neuropathology, Heinrich Heine University, Medical Faculty, Düsseldorf, Germany
| | | | - Marion Smits
- 3Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam
| | - Christian Senft
- 8Department of Neurosurgery, and
- 9Department of Neurosurgery, Friedrich Schiller University, Medical Faculty, Jena, Germany
| | | | | | - Martin Voss
- 10Dr. Senckenberg Institute of Neurooncology, Goethe University, Medical Faculty, Frankfurt; and
| | | | - Marcel A Kamp
- Departments of1Neurosurgery and
- 9Department of Neurosurgery, Friedrich Schiller University, Medical Faculty, Jena, Germany
| |
Collapse
|
45
|
Cerasa A, Tartarisco G, Bruschetta R, Ciancarelli I, Morone G, Calabrò RS, Pioggia G, Tonin P, Iosa M. Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics. Biomedicines 2022; 10:biomedicines10092267. [PMID: 36140369 PMCID: PMC9496389 DOI: 10.3390/biomedicines10092267] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/29/2022] Open
Abstract
Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke and traumatic brain injury (TBI). Thirteen papers directly addressing the different performance among ML and LR methods were included in this review. Basically, ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury. Better performance of specific ML algorithms (such as Artificial neural networks) was mainly described in the stroke domain, but the high heterogeneity in features extracted from low-dimensional clinical data reduces the enthusiasm for applying this powerful method in clinical practice. To better capture and predict the dynamic changes in patients with brain injury during intensive care courses ML algorithms should be extended to high-dimensional data extracted from neuroimaging (structural and fMRI), EEG and genetics.
Collapse
Affiliation(s)
- Antonio Cerasa
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Rende, Italy
- S. Anna Institute, 88900 Crotone, Italy
- Correspondence:
| | - Gennaro Tartarisco
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
| | - Roberta Bruschetta
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Irene Ciancarelli
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
- San Raffaele Sulmona Institute, 67039 Sulmona, Italy
| | | | - Giovanni Pioggia
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
| | | | - Marco Iosa
- IRCCS Centro Neurolesi “Bonino-Pulejo”, 98123 Messina, Italy
- Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy
- Santa Lucia Foundation IRCSS, 00179 Rome, Italy
| |
Collapse
|
46
|
Volovici V, Syn NL, Ercole A, Zhao JJ, Liu N. Steps to avoid overuse and misuse of machine learning in clinical research. Nat Med 2022; 28:1996-1999. [PMID: 36097217 DOI: 10.1038/s41591-022-01961-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Victor Volovici
- Department of Neurosurgery, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
| | - Nicholas L Syn
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Surgery, National University Hospital, National University Health System, Singapore, Singapore
| | - Ari Ercole
- Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK
| | - Joseph J Zhao
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| |
Collapse
|
47
|
Fusco A, Galluccio C, Castelli L, Pazzaglia C, Pastorino R, Pires Marafon D, Bernabei R, Giovannini S, Padua L. Severe Acquired Brain Injury: Prognostic Factors of Discharge Outcome in Older Adults. Brain Sci 2022; 12:brainsci12091232. [PMID: 36138968 PMCID: PMC9496921 DOI: 10.3390/brainsci12091232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/01/2022] [Accepted: 09/08/2022] [Indexed: 11/16/2022] Open
Abstract
Severe Acquired Brain Injury (sABI) is a leading cause of disability and requires intensive rehabilitation treatment. Discharge from the rehabilitation ward is a key moment in patient management. Delays in patient discharge can adversely affect hospital productivity and increase healthcare costs. The discharge should be structured from the hospital admission toward the most appropriate environment. The purpose of our study is to investigate early predictors of outcome for discharge in older adults with sABI. A retrospective study was performed on 22 patients who were admitted to an intensive neurorehabilitation unit between June 2019 and December 2021. Patients were divided into two outcome categories, good outcome (GO) or poor outcome (PO), based on discharge destination, and the possible prognostic factors were analyzed at one and two months after admission. Among the factors analyzed, changes in the Disability Rating Scale (DRS) and Level of Cognitive Functioning (LCF) at the first and second month of hospitalization were predictive of GO at discharge (DRS, p = 0.025; LCF, p = 0.011). The presence of percutaneous endoscopic gastrostomy at two months after admission was also significantly associated with PO (p = 0.038). High Body Mass Index (BMI) and the presence of sepsis at one month after admission were possible predictors of PO (BMI p = 0.048; sepsis p = 0.014). An analysis of dynamic predictors could be useful to guarantee an early evaluation of hospital discharge in frail patients with sABI.
Collapse
Affiliation(s)
- Augusto Fusco
- UOC Neuroriabilitazione ad Alta Intensità, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Caterina Galluccio
- UOC Neuroriabilitazione ad Alta Intensità, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Department of Geriatrics and Orthopaedics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Letizia Castelli
- UOC Neuroriabilitazione ad Alta Intensità, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Department of Aging, Neurological, Orthopaedic and Head-Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Costanza Pazzaglia
- UOC Neuroriabilitazione ad Alta Intensità, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Roberta Pastorino
- Department of Woman and Child Health and Public Health—Public Health Area, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Denise Pires Marafon
- Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Roberto Bernabei
- Department of Geriatrics and Orthopaedics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Department of Aging, Neurological, Orthopaedic and Head-Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Silvia Giovannini
- Department of Geriatrics and Orthopaedics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- UOS Neuroriabilitazione Post-acuzie, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Correspondence: ; Tel.: +39-06-3015-4382
| | - Luca Padua
- UOC Neuroriabilitazione ad Alta Intensità, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Department of Geriatrics and Orthopaedics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| |
Collapse
|
48
|
Liew BXW, Kovacs FM, Rügamer D, Royuela A. Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:2082-2091. [PMID: 35353221 DOI: 10.1007/s00586-022-07188-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/29/2022] [Accepted: 03/12/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional regression techniques, when developing prognostic models in individuals with neck pain. METHODS A total of 3001 participants suffering from neck pain were included into a clinical registry database. Three dichotomous outcomes of a clinically meaningful improvement in neck pain, arm pain, and disability at 3 months follow-up were used. There were 26 predictors included, five numeric and 21 categorical. Seven modelling techniques were used (logistic regression, least absolute shrinkage and selection operator [LASSO], gradient boosting [Xgboost], K nearest neighbours [KNN], support vector machine [SVM], random forest [RF], and artificial neural networks [ANN]). The primary measure of model performance was the area under the receiver operator curve (AUC) of the validation set. RESULTS The ML algorithm with the greatest AUC for predicting arm pain (AUC = 0.765), neck pain (AUC = 0.726), and disability (AUC = 0.703) was Xgboost. The improvement in classification AUC from stepwise logistic regression to the best performing machine learning algorithms was 0.081, 0.103, and 0.077 for predicting arm pain, neck pain, and disability, respectively. CONCLUSION The improvement in prediction performance between ML and logistic regression methods in the present study, could be due to the potential greater nonlinearity between baseline predictors and clinical outcome. The benefit of machine learning in prognostic modelling may be dependent on factors like sample size, variable type, and disease investigated.
Collapse
Affiliation(s)
- Bernard X W Liew
- School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, UK.
| | - Francisco M Kovacs
- Unidad de la Espalda Kovacs, Hospital Universitario HLA-Moncloa. University Hospital, Avenida de Menéndez Pelayo, 67, 28009, Madrid, Spain
| | - David Rügamer
- Department of Statistics, Ludwig-Maximilians-Universität München, München, Germany
| | - Ana Royuela
- Biostatistics Unit. Hospital Puerta de Hierro, IDIPHISA, CIBERESP, REIDE, Madrid, Spain
| |
Collapse
|
49
|
Adil SM, Elahi C, Patel DN, Seas A, Warman PI, Fuller AT, Haglund MM, Dunn TW. Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting. World Neurosurg 2022; 164:e8-e16. [PMID: 35247613 DOI: 10.1016/j.wneu.2022.02.097] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms. METHODS TBI patients' data were prospectively collected in Kampala, Uganda, from 2016 to 2020. To predict good versus poor outcome at hospital discharge, we created deep neural network, shallow neural network, and elastic-net regularized logistic regression models. Predictors included 13 easily acquirable clinical variables. We assessed model performance with 5-fold cross-validation to calculate areas under both the receiver operating characteristic curve and precision-recall curve (AUPRC), in addition to standardized partial AUPRC to focus on comparisons at clinically relevant operating points. RESULTS We included 2164 patients for model training, of which 12% had poor outcomes. The deep neural network performed best as measured by the area under the receiver operating characteristic curve (0.941) and standardized partial AUPRC in region maximizing recall (0.291), whereas the shallow neural network was best by the area under the precision-recall curve (0.770). In several other comparisons, the elastic-net regularized logistic regression was noninferior to the neural networks. CONCLUSIONS We present the first use of deep learning for TBI prognostication, with an emphasis on LMICs, where there is great need for decision support to allocate limited resources. Optimal algorithm selection depends on the specific clinical setting; deep learning is not a panacea, though it may have a role in these efforts.
Collapse
Affiliation(s)
- Syed M Adil
- Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
| | - Cyrus Elahi
- Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Dev N Patel
- Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Department of Neurosurgery, New York University Langone Medical Center, New York, New York, USA
| | - Andreas Seas
- Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Pranav I Warman
- Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA
| | - Anthony T Fuller
- Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
| | - Michael M Haglund
- Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
| | - Timothy W Dunn
- Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA.
| |
Collapse
|
50
|
Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review. Ann Emerg Med 2022; 80:440-455. [PMID: 35842343 DOI: 10.1016/j.annemergmed.2022.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/20/2022] [Accepted: 05/04/2022] [Indexed: 11/23/2022]
Abstract
STUDY OBJECTIVE Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research. METHODS We conducted a systematic review from inception to May 2021, using Embase, MEDLINE through Ovid, Web of Science, Google Scholar, and relevant gray literature, for uses of machine learning in predicting the outcomes of trauma patients. The screening and data extraction were performed by 2 independent reviewers. RESULTS Of the 14,694 identified articles screened, 67 were included for data extraction. Artificial neural networks comprised the most commonly used model, and mortality was the most prevalent outcome of interest. In terms of machine learning model development, there was a lack of studies that employed external validation, feature selection methods, and performed formal calibration testing. Significant heterogeneity in reporting was also observed between the machine learning models employed, patient populations, performance metrics, and features employed. CONCLUSION This review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.
Collapse
|