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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.
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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
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Sikora A, Zhao B, Kong Y, Murray B, Shen Y. Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.18.23295724. [PMID: 37790491 PMCID: PMC10543219 DOI: 10.1101/2023.09.18.23295724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Rationale Duration of mechanical ventilation is associated with adverse outcomes in critically ill patients and increased use of resources. The increasing complexity of medication regimens has been associated with increased mortality, length of stay, and fluid overload but has never been studied specifically in the setting of mechanical ventilation. Objective The purpose of this analysis was to develop prediction models for mechanical ventilation duration to test the hypothesis that incorporating medication data may improve model performance. Methods This was a retrospective cohort study of adults admitted to the ICU and undergoing mechanical ventilation for longer than 24 hours from October 2015 to October 2020. Patients were excluded if it was not their index ICU admission or if the patient was placed on comfort care in the first 24 hours of admission. Relevant patient characteristics including age, sex, body mass index, admission diagnosis, morbidities, vital signs measurements, severity of illness, medication regimen complexity as measured by the MRC-ICU, and medical treatments before intubation were collected. The primary outcome was area under the receiver operating characteristic (AUROC) of prediction models for prolonged mechanical ventilation (defined as greater than 5 days). Both logistic regression and supervised learning techniques including XGBoost, Random Forest, and Support Vector Machine were used to develop prediction models. Results The 318 patients [age 59.9 (SD 16.9), female 39.3%, medical 28.6%] had mean 24-hour MRC-ICU score of 21.3 (10.5), mean APACHE II score of 21.0 (5.4), mean SOFA score of 9.9 (3.3), and ICU mortality rate of 22.6% (n=72). The strongest performing logistic model was the base model with MRC-ICU added, with AUROC of 0.72, positive predictive value (PPV) of 0.83, and negative prediction value (NPV) of 0.92. The strongest overall model was Random Forest with an AUROC of 0.78, a PPV of 0.53, and NPV of 0.90. Feature importance analysis using support vector machine and Random Forest revealed severity of illness scores and medication related data were the most important predictors. Conclusions Medication regimen complexity is significantly associated with prolonged duration of mechanical ventilation in critically ill patients, and prediction models incorporating medication information showed modest improvement in this prediction.
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Affiliation(s)
- Andrea Sikora
- University of Georgia College of Pharmacy, Department of Clinical and Administrative Pharmacy, Augusta, GA, USA
| | - Bokai Zhao
- University of Georgia College of Public Health, Epidemiology & Biostatistics, Athens, GA, USA
| | - Yanlei Kong
- Renmin University of China, School of Statistics, Beijing, China
| | - Brian Murray
- University of North Carolina Medical Center, Department of Pharmacy, Chapel Hill, NC, USA
| | - Ye Shen
- University of Georgia College of Public Health, Epidemiology & Biostatistics, Athens, GA, USA
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Vali M, Paydar S, Seif M, Sabetian G, Abujaber A, Ghaem H. Prediction prolonged mechanical ventilation in trauma patients of the intensive care unit according to initial medical factors: a machine learning approach. Sci Rep 2023; 13:5925. [PMID: 37045979 PMCID: PMC10097728 DOI: 10.1038/s41598-023-33159-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 04/07/2023] [Indexed: 04/14/2023] Open
Abstract
The goal of this study was to develop a predictive machine learning model to predict the risk of prolonged mechanical ventilation (PMV) in patients admitted to the intensive care unit (ICU), with a focus on laboratory and Arterial Blood Gas (ABG) data. This retrospective cohort study included ICU patients admitted to Rajaei Hospital in Shiraz between 2016 and March 20, 2022. All adult patients requiring mechanical ventilation and seeking ICU admission had their data analyzed. Six models were created in this study using five machine learning models (PMV more than 3, 5, 7, 10, 14, and 23 days). Patients' demographic characteristics, Apache II, laboratory information, ABG, and comorbidity were predictors. This study used Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and C.5 decision tree (C.5 DT) to predict PMV. The study enrolled 1138 eligible patients, excluding brain-dead patients and those without mechanical ventilation or a tracheostomy. The model PMV > 14 days showed the best performance (Accuracy: 83.63-98.54). The essential ABG variables in our two optimal models (artificial neural network and decision tree) in the PMV > 14 models include FiO2, paCO2, and paO2. This study provides evidence that machine learning methods outperform traditional methods and offer a perspective for achieving a consensus definition of PMV. It also introduces ABG and laboratory information as the two most important variables for predicting PMV. Therefore, there is significant value in deploying such models in clinical practice and making them accessible to clinicians to support their decision-making.
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Affiliation(s)
- Mohebat Vali
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahram Paydar
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mozhgan Seif
- Non-Communicable Research Center, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Golnar Sabetian
- Anesthesiology and Critical Care Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Haleh Ghaem
- Non-Communicable Diseases Research Center, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
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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.
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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
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Hunter OF, Perry F, Salehi M, Bandurski H, Hubbard A, Ball CG, Morad Hameed S. Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care. World J Emerg Surg 2023; 18:16. [PMID: 36879293 PMCID: PMC9987401 DOI: 10.1186/s13017-022-00469-1] [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: 10/29/2022] [Accepted: 12/12/2022] [Indexed: 03/08/2023] Open
Abstract
Artificial intelligence (AI) and machine learning describe a broad range of algorithm types that can be trained based on datasets to make predictions. The increasing sophistication of AI has created new opportunities to apply these algorithms within within trauma care. Our paper overviews the current uses of AI along the continuum of trauma care, including injury prediction, triage, emergency department volume, assessment, and outcomes. Starting at the point of injury, algorithms are being used to predict severity of motor vehicle crashes, which can help inform emergency responses. Once on the scene, AI can be used to help emergency services triage patients remotely in order to inform transfer location and urgency. For the receiving hospital, these tools can be used to predict trauma volumes in the emergency department to help allocate appropriate staffing. After patient arrival to hospital, these algorithms not only can help to predict injury severity, which can inform decision-making, but also predict patient outcomes to help trauma teams anticipate patient trajectory. Overall, these tools have the capability to transform trauma care. AI is still nascent within the trauma surgery sphere, but this body of the literature shows that this technology has vast potential. AI-based predictive tools in trauma need to be explored further through prospective trials and clinical validation of algorithms.
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Affiliation(s)
- Olivia F Hunter
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Frances Perry
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Mina Salehi
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | | | - Alan Hubbard
- University of California, Berkeley School of Public Health, Berkeley, USA
| | - Chad G Ball
- Department of Surgery, University of Calgary, Calgary, Canada
| | - S Morad Hameed
- Department of Surgery, University of British Columbia, Vancouver, Canada. .,T6 Health Systems, Boston, USA.
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Dang H, Su W, Tang Z, Yue S, Zhang H. Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: A data-based study. Front Neurosci 2023; 16:1031712. [PMID: 36741050 PMCID: PMC9892718 DOI: 10.3389/fnins.2022.1031712] [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: 08/30/2022] [Accepted: 12/30/2022] [Indexed: 01/20/2023] Open
Abstract
Objective Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. In this study, the characteristics of the patients, who were admitted to the China Rehabilitation Research Center, were elucidated in the TBI database, and a prediction model based on the Fugl-Meyer assessment scale (FMA) was established using this database. Methods A retrospective analysis of 463 TBI patients, who were hospitalized from June 2016 to June 2020, was performed. The data of the patients used for this study included the age and gender of the patients, course of TBI, complications, and concurrent dysfunctions, which were assessed using FMA and other measures. The information was collected at the time of admission to the hospital and 1 month after hospitalization. After 1 month, a prediction model, based on the correlation analyses and a 1-layer genetic algorithms modified back propagation (GA-BP) neural network with 175 patients, was established to predict the FMA. The correlations between the predicted and actual values of 58 patients (prediction set) were described. Results Most of the TBI patients, included in this study, had severe conditions (70%). The main causes of the TBI were car accidents (56.59%), while the most common complication and dysfunctions were hydrocephalus (46.44%) and cognitive and motor dysfunction (65.23 and 63.50%), respectively. A total of 233 patients were used in the prediction model, studying the 11 prognostic factors, such as gender, course of the disease, epilepsy, and hydrocephalus. The correlation between the predicted and the actual value of 58 patients was R 2 = 0.95. Conclusion The genetic algorithms modified back propagation neural network can predict motor function in patients with traumatic brain injury, which can be used as a reference for risk and prognosis assessment and guide clinical decision-making.
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Affiliation(s)
- Hui Dang
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,China Rehabilitation Research Center, Beijing, China,School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong, China
| | - Wenlong Su
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong, China,China Rehabilitation Research Center, School of Rehabilitation, Capital Medical University, Beijing, China
| | - Zhiqing Tang
- China Rehabilitation Research Center, School of Rehabilitation, Capital Medical University, Beijing, China
| | - Shouwei Yue
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,*Correspondence: Shouwei Yue,
| | - Hao Zhang
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong, China,China Rehabilitation Research Center, School of Rehabilitation, Capital Medical University, Beijing, China,Hao Zhang,
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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.
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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
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Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:2053-2061. [PMID: 35989154 DOI: 10.1016/j.jval.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/10/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
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Affiliation(s)
- Woojung Lee
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
| | - Naomi Schwartz
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Sara Khor
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Noah Hammarlund
- Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Beth Devine
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
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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.
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Goo ZQ, Muthusamy KA. Early versus standard tracheostomy in ventilated patients in neurosurgical intensive care unit: A randomized controlled trial. J Clin Neurosci 2022; 98:162-167. [PMID: 35182846 DOI: 10.1016/j.jocn.2022.02.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Tracheostomy is performed in patients with prolonged mechanical ventilation, who suffered catastrophic neurologic insult or upper airway obstruction. Thus far, there is no consensus on the optimal timing in performing a tracheostomy. This study aims to test whether early tracheostomy in mechanically ventilated patients in a neurosurgical setting would be associated with a shorter time of mechanical ventilation as compared to standard tracheostomy. METHODS This single-center prospective randomized controlled trial was conducted at University Malaya Medical Centre from July 2019 to July 2021. The likelihood of prolonged ventilation was determined objectively using the TRACH score and the patient's clinical presentation. The outcomes measured were days of mechanical ventilation post-tracheostomy, days of neuro-intensive care unit stay, and days of hospital stay. Tracheostomy-related complications were collected. The data collected were analyzed using Statistical Package for the Social Sciences version 25 for Windows (SPSS Inc., Chicago, IL, USA). RESULTS In all, 39 patients were randomly assigned. Of these, 20 were allocated to the early tracheostomy group (ET) and 19 were allocated to the standard tracheostomy group (ST). The demographic characteristics were similar between the groups. The primary outcome, mean (SD) days of mechanical ventilation post-tracheostomy, was statistically different in the 2 groups- early 11.9 (9.3) days, standard 18.9 (32.5) days; p = 0.014. There were comparable tracheostomy-related complications in both groups. CONCLUSION Early tracheostomy is associated with a shorter duration of mechanical ventilation in a neurosurgical intensive care unit setting.
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Affiliation(s)
- Zhen Qiang Goo
- Division of General Surgery, Department of Surgery, University Malaya Medical Centre, Kuala Lumpur, Malaysia.
| | - Kalai Arasu Muthusamy
- Division of Neurosurgery, Department of Surgery, University Malaya Medical Centre, Kuala Lumpur, Malaysia.
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Lee KC, Lin TC, Chiang HF, Horng GJ, Hsu CC, Wu NC, Su HC, Chen KT. Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning. Medicine (Baltimore) 2021; 100:e27753. [PMID: 34889225 PMCID: PMC8663914 DOI: 10.1097/md.0000000000027753] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 10/27/2021] [Indexed: 01/05/2023] Open
Abstract
In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-making involving patients with severe trauma. We aimed to develope a machine learning-based early prognostic model based on admission features and initial ED management.We only recruited patients with severe trauma (defined as an injury severity score >15) as the study cohort and excluded children (defined as patients <16 years old) from a 4-years database (Chi-Mei Medical Center, from January 2015, to December 2018) recording the clinical features of all admitted trauma patients. We considered only patient features that could be determined within the first 2 hours after arrival to the ED. These variables included Glasgow Coma Scale (GCS) score; heart rate; respiratory rate; mean arterial pressure (MAP); prehospital cardiac arrest; abbreviated injury scales (AIS) of head and neck, thorax, and abdomen; and ED interventions (tracheal intubation/tracheostomy, blood product transfusion, thoracostomy, and cardiopulmonary resuscitation). The endpoint for prognostic analyses was mortality within 7 days of admission.We divided the study cohort into the early death group (149 patients who died within 7 days of admission) and non-early death group (2083 patients who survived at >7 days of admission). The extreme Gradient Boosting (XGBoost) machine learning model provided mortality prediction with higher accuracy (94.0%), higher sensitivity (98.0%), moderate specificity (54.8%), higher positive predict value (PPV) (95.4%), and moderate negative predictive value (NPV) (74.2%).We developed a machine learning-based prognostic model that showed high accuracy, high sensitivity, and high PPV for predicting the mortality of patients with severe trauma.
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Affiliation(s)
- Kuo-Chang Lee
- Emergency Department, Chi-Mei Medical Center, Tainan, Taiwan
| | - Tzu-Chieh Lin
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan
| | - Hsiu-Fen Chiang
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan
| | - Gwo-Jiun Horng
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan
| | - Chien-Chin Hsu
- Emergency Department, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Biotechnology, Southern Tainan University of Technology, Tainan, Taiwan
| | - Nan-Chun Wu
- Division of Traumatology, Department of Surgery, Chi-Mei Medical Center, Tainan, Taiwan
| | - Hsiu-Chen Su
- Division of Traumatology, Department of Surgery, Chi-Mei Medical Center, Tainan, Taiwan
| | - Kuo-Tai Chen
- Emergency Department, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Ossai CI, Wickramasinghe N. Intelligent decision support with machine learning for efficient management of mechanical ventilation in the intensive care unit - A critical overview. Int J Med Inform 2021; 150:104469. [PMID: 33906020 DOI: 10.1016/j.ijmedinf.2021.104469] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 04/16/2021] [Accepted: 04/18/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Effective management of Mechanical Ventilation (MV) is vital for reducing morbidity, mortality, and cost of healthcare. OBJECTIVE This study aims to synthesize evidence for effective MV management through Intelligent decision support (IDS) with Machine Learning (ML). METHOD Databases that include EBSCO, IEEEXplore, Google Scholar, SCOPUS, and the Web of Science were systematically searched to identify studies on IDS for effective MV management regarding Tidal Volume (TV), asynchrony, weaning, and other outcomes such as the risk of Prolonged Mechanical ventilation (PMV). The quality of the articles identified was assessed with a modified Joanna Briggs Institute (JBI) critical appraisal checklist for cross-sessional research. RESULTS A total of 26 articles were identified for the study that has IDS for TV (n = 2, 7.8 %), asynchrony (n = 9, 34.6 %), weaning (n = 12, 46.2 %), and others (n = 3, 11.5 %). It was affirmed that implementing IDS in MV management will enhance seamless ICU patient management following the utilization of various Machine Learning (ML) algorithms in decision support. The studies relied on (n = 14) ML algorithms to predict the TV, asynchrony, weaning, risk of PMV and Positive End-Expiratory Pressure (PEEP) changes of 11-20262 ICU patients records with model inputs ranging from (n = 1) for timeseries analysis of TV to (n = 47) for weaning prediction. CONCLUSIONS The small data size, poor study design, and result reporting, with the heterogeneity of techniques used in the various studies, hampered the development of a unified approach for managing MV efficiency in TV monitoring, asynchrony, and weaning predictions. Notwithstanding, the ensemble model was able to predict TV, asynchrony, and weaning to a higher accuracy than the other algorithms.
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Affiliation(s)
- Chinedu I Ossai
- Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John street Hawthorn, Victoria, 3122, Australia.
| | - Nilmini Wickramasinghe
- Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John street Hawthorn, Victoria, 3122, Australia; Epworth Healthcare Australia, Australia.
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Douthit BJ, Musser RC, Lytle KS, Richesson RL. A Closer Look at the "Right" Format for Clinical Decision Support: Methods for Evaluating a Storyboard BestPractice Advisory. J Pers Med 2020; 10:jpm10040142. [PMID: 32977564 PMCID: PMC7712422 DOI: 10.3390/jpm10040142] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 01/17/2023] Open
Abstract
(1) Background: The five rights of clinical decision support (CDS) are a well-known framework for planning the nuances of CDS, but recent advancements have given us more options to modify the format of the alert. One-size-fits-all assessments fail to capture the nuance of different BestPractice Advisory (BPA) formats. To demonstrate a tailored evaluation methodology, we assessed a BPA after implementation of Storyboard for changes in alert fatigue, behavior influence, and task completion; (2) Methods: Data from 19 weeks before and after implementation were used to evaluate differences in each domain. Individual clinics were evaluated for task completion and compared for changes pre- and post-redesign; (3) Results: The change in format was correlated with an increase in alert fatigue, a decrease in erroneous free text answers, and worsened task completion at a system level. At a local level, however, 14% of clinics had improved task completion; (4) Conclusions: While the change in BPA format was correlated with decreased performance, the changes may have been driven primarily by the COVID-19 pandemic. The framework and metrics proposed can be used in future studies to assess the impact of new CDS formats. Although the changes in this study seemed undesirable in aggregate, some positive changes were observed at the level of individual clinics. Personalized implementations of CDS tools based on local need should be considered.
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Affiliation(s)
- Brian J. Douthit
- School of Nursing, Duke University, Durham, NC 27710, USA;
- Correspondence:
| | - R. Clayton Musser
- School of Medicine, Duke University, Durham, NC 27710, USA;
- Duke Health, Duke School of Medicine, Durham, NC 27710 USA;
| | - Kay S. Lytle
- Duke Health, Duke School of Medicine, Durham, NC 27710 USA;
| | - Rachel L. Richesson
- School of Nursing, Duke University, Durham, NC 27710, USA;
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA
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van Teijlingen A, Tuttle T, Bouchachia H, Sathian B, van Teijlingen E. Artificial Intelligence and Health in Nepal. Nepal J Epidemiol 2020; 10:915-918. [PMID: 33042595 PMCID: PMC7538016 DOI: 10.3126/nje.v10i3.31649] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/27/2020] [Accepted: 09/27/2020] [Indexed: 11/17/2022] Open
Abstract
The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago. There has been a technological revolution of big data and Artificial Intelligence (AI). Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI. This Short Communication starts with an overview of the key terms, including AI, machine learning, deep learning and Big Data. This Short Communication highlights so developments of AI in health that could benefit a low-income country like Nepal and stresses the need for Nepal's health and education systems to track such developments and apply them locally. Moreover, Nepal needs to start growing its own AI expertise to help develop national or South Asian solutions. This would require investing in local resources such as access to computer power/capacity as well as training young Nepali to work in AI.
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Affiliation(s)
| | - Tell Tuttle
- Department of Pure and Applied Chemistry, Strathclyde University, Glasgow, UK
| | - Hamid Bouchachia
- Department of Computing & Informatics, Bournemouth University, Bournemouth, UK
| | | | - Edwin van Teijlingen
- Correspondence: Dr. Edwin van Teijlingen, Professor, Centre for Midwifery, Maternal and Perinatal Health, Bournemouth University, UK. Email:
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