1
|
Cichosz SL, Olesen SS, Jensen MH. Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring. J Diabetes Sci Technol 2024:19322968241286907. [PMID: 39377175 DOI: 10.1177/19322968241286907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/09/2024]
Abstract
BACKGROUND AND OBJECTIVE The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action. METHODS We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages. RESULTS A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes. CONCLUSION Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.
Collapse
Affiliation(s)
- Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Søren Schou Olesen
- Department of Clinical Medicine, Faculty of Medicine, Aalborg University Hospital, Aalborg, Denmark
- Mech-Sense, Centre for Pancreatic Diseases, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark
| | - Morten Hasselstrøm Jensen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
- Data Science, Novo Nordisk A/S, Søborg, Denmark
| |
Collapse
|
2
|
Halvorson BD, Ward AD, Murrell D, Lacefield JC, Wiseman RW, Goldman D, Frisbee JC. Regulation of Skeletal Muscle Resistance Arteriolar Tone: Temporal Variability in Vascular Responses. J Vasc Res 2024:1-29. [PMID: 39362208 DOI: 10.1159/000541169] [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: 06/27/2024] [Accepted: 08/25/2024] [Indexed: 10/05/2024] Open
Abstract
INTRODUCTION A full understanding of the integration of the mechanisms of vascular tone regulation requires an interrogation of the temporal behavior of arterioles across vasoactive challenges. Building on previous work, the purpose of the present study was to start to interrogate the temporal nature of arteriolar tone regulation with physiological stimuli. METHODS We determined the response rate of ex vivo proximal and in situ distal resistance arterioles when challenged by one-, two-, and three-parameter combinations of five major physiological stimuli (norepinephrine, intravascular pressure, oxygen, adenosine [metabolism], and intralumenal flow). Predictive machine learning models determined which factors were most influential in controlling the rate of arteriolar responses. RESULTS Results indicate that vascular response rate is dependent on the intensity of the stimulus used and can be severely hindered by altered environments, caused by application of secondary or tertiary stimuli. Advanced analytics suggest that adrenergic influences were dominant in predicting proximal arteriolar response rate compared to metabolic influences in distal arterioles. CONCLUSION These data suggest that the vascular response rate to physiologic stimuli can be strongly influenced by the local environment. Translating how these effects impact vascular networks is imperative for understanding how the microcirculation appropriately perfuses tissue across conditions.
Collapse
Affiliation(s)
- Brayden D Halvorson
- Departments of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Aaron D Ward
- Departments of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Departments of Oncology, University of Western Ontario, London, Ontario, Canada
| | - Donna Murrell
- Departments of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Departments of Oncology, University of Western Ontario, London, Ontario, Canada
| | - James C Lacefield
- Departments of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- School of Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
| | - Robert W Wiseman
- Departments of Physiology and Radiology, Michigan State University, East Lansing, Michigan, USA
| | - Daniel Goldman
- Departments of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Jefferson C Frisbee
- Departments of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| |
Collapse
|
3
|
Stipek C, Hauser T, Adams D, Epting J, Brelsford C, Moehl J, Dias P, Piburn J, Stewart R. Inferring building height from footprint morphology data. Sci Rep 2024; 14:18651. [PMID: 39134571 PMCID: PMC11319631 DOI: 10.1038/s41598-024-66467-2] [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: 04/29/2024] [Accepted: 07/01/2024] [Indexed: 08/15/2024] Open
Abstract
As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.
Collapse
Affiliation(s)
- Clinton Stipek
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
| | - Taylor Hauser
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Daniel Adams
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Justin Epting
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | | | - Jessica Moehl
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Philipe Dias
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jesse Piburn
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Robert Stewart
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| |
Collapse
|
4
|
Deng L, Zhao J, Wang T, Liu B, Jiang J, Jia P, Liu D, Li G. Construction and validation of predictive models for intravenous immunoglobulin-resistant Kawasaki disease using an interpretable machine learning approach. Clin Exp Pediatr 2024; 67:405-414. [PMID: 39048087 PMCID: PMC11298769 DOI: 10.3345/cep.2024.00549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/27/2024] [Accepted: 05/10/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development. PURPOSE This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. METHODS Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. RESULTS Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. CONCLUSION Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.
Collapse
Affiliation(s)
- Linfan Deng
- Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, China
- Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, China
| | - Jian Zhao
- Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, China
| | - Ting Wang
- Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, China
| | - Bin Liu
- Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, China
| | - Jun Jiang
- Department of General Surgery (Thyroid Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Diseases Key Laboratory of Sichuan Province, Luzhou, China
| | - Peng Jia
- Department of Pediatrics, Zigong First People’s Hospital, Zigong, China
| | - Dong Liu
- Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, China
| | - Gang Li
- Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, China
| |
Collapse
|
5
|
Guo Y, Huang C, Sheng Y, Zhang W, Ye X, Lian H, Xu J, Chen Y. Improve the efficiency and accuracy of ophthalmologists' clinical decision-making based on AI technology. BMC Med Inform Decis Mak 2024; 24:192. [PMID: 38982465 PMCID: PMC11234671 DOI: 10.1186/s12911-024-02587-z] [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: 03/02/2023] [Accepted: 06/24/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. METHODS In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. RESULTS The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. CONCLUSION The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.
Collapse
Affiliation(s)
- Yingxuan Guo
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Changke Huang
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yaying Sheng
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Wenjie Zhang
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xin Ye
- Center for Rehabilitation Medicine, Department of Ophthalmology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
| | - Hengli Lian
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jiahao Xu
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yiqi Chen
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
- Center for Rehabilitation Medicine, Department of Ophthalmology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China.
| |
Collapse
|
6
|
Ding R, Deng M, Wei H, Zhang Y, Wei L, Jiang G, Zhu H, Huang X, Fu H, Zhao S, Yuan H. Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series. CNS Neurosci Ther 2024; 30:e14848. [PMID: 38973193 PMCID: PMC11228354 DOI: 10.1111/cns.14848] [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: 03/20/2024] [Revised: 06/16/2024] [Accepted: 06/27/2024] [Indexed: 07/09/2024] Open
Abstract
AIMS To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes. METHODS Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm. RESULTS The analysis included 1085 TBI patients. Compared to individual models and existing scoring systems, the combination of EHR and PTS features demonstrated comparable or even superior performance in predicting in-hospital mortality (AUROC = 0.878), neurological outcomes (AUROC = 0.877), and PLOS (AUROC = 0.835). The model's performance was validated in the MIMIC III dataset, and SHAP algorithms identified six key intervention points for EHR features related to prognostic outcomes. Moreover, the EHR results (All AUROC >0.8) were translated into online tools for clinical use. CONCLUSION Our study highlights the importance of early-stage PTS signals in predicting TBI patient outcomes. The integration of interpretable algorithms and simplified prediction tools can support treatment decision-making, contributing to the development of accurate prediction models and timely clinical intervention.
Collapse
Affiliation(s)
- Ruifeng Ding
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Mengqiu Deng
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Huawei Wei
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yixuan Zhang
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Liangtian Wei
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, China
| | - Guowei Jiang
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Hongwei Zhu
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Xingshuai Huang
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Hailong Fu
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Shuang Zhao
- Department of Anesthesiology, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China
| | - Hongbin Yuan
- Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| |
Collapse
|
7
|
Yoo JW, Park J, Park H. Enhancing safety of construction workers in Korea: an integrated text mining and machine learning framework for predicting accident types. Int J Inj Contr Saf Promot 2024; 31:203-215. [PMID: 38164519 DOI: 10.1080/17457300.2023.2300424] [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: 07/26/2023] [Accepted: 12/24/2023] [Indexed: 01/03/2024]
Abstract
Construction workers face a high risk of various occupational accidents, many of which can result in fatalities. This study aims to develop a prediction model for nine prevalent types of construction accidents, utilizing construction tasks, activities, and tools/materials as input features, through the application of machine learning-based multi-class classification algorithms. 152,867 construction accident summary reports, composed of both structured (construction task, construction activity, accident type) and unstructured data (tools/materials) were used for the study. The study employed several data processing techniques, including keyword extraction through text mining, Boruta feature selection, and SMOTE data resampling enhance model accuracy. Three performance metrics (Multi-class area under the receiver operating characteristic curve (MAUC), Multi-class Matthews Correlation Coefficient (MMCC), Geometric-mean (G-mean)) were used to compare the predictive performance of four machine learning algorithms, including Decision tree, Random forest, Naïve bayes, and XGBoost. Of the four algorithms, XGBoost showed the highest performance in predicting accident type (MAUC: 0.8603, MMCC: 0.3523, G-mean: 0.5009). Furthermore, a Shapley additive explanation (SHAP) analysis was conducted to visualize feature importance. The findings of this study make a valuable contribution to improving construction safety by presenting a prediction model for accident types derived from real-world big data.
Collapse
Affiliation(s)
- Joon Woo Yoo
- Department of Industrial Engineering, Yonsei University, Seoul, South Korea
| | - Junsung Park
- Department of Industrial Engineering, Yonsei University, Seoul, South Korea
| | - Heejun Park
- Department of Industrial Engineering, Yonsei University, Seoul, South Korea
| |
Collapse
|
8
|
Zhu G, Ozkara BB, Chen H, Zhou B, Jiang B, Ding VY, Wintermark M. Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study. Neuroradiol J 2024; 37:74-83. [PMID: 37921691 PMCID: PMC10863571 DOI: 10.1177/19714009231212364] [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] [Indexed: 11/04/2023] Open
Abstract
PURPOSE We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients. METHODS In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables. RESULTS Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis. CONCLUSION Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.
Collapse
Affiliation(s)
- Guangming Zhu
- Department of Neurology, The University of Arizona, USA
| | - Burak B Ozkara
- Department of Neuroradiology, MD Anderson Cancer Center, USA
| | - Hui Chen
- Department of Neuroradiology, MD Anderson Cancer Center, USA
| | - Bo Zhou
- Neuroradiology Division, Department of Radiology, Stanford University, USA
| | - Bin Jiang
- Neuroradiology Division, Department of Radiology, Stanford University, USA
| | - Victoria Y Ding
- Quantitative Sciences Unit, Department of Medicine, Stanford University, USA
| | - Max Wintermark
- Department of Neuroradiology, MD Anderson Cancer Center, USA
| |
Collapse
|
9
|
Wei W, Mengshan L, Yan W, Lixin G. Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine. BMC Chem 2024; 18:24. [PMID: 38291518 PMCID: PMC11367823 DOI: 10.1186/s13065-024-01127-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/15/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Clusters, a novel hierarchical material structure that emerges from atoms or molecules, possess unique reactivity and catalytic properties, crucial in catalysis, biomedicine, and optoelectronics. Predicting cluster energy provides insights into electronic structure, magnetism, and stability. However, the structure of clusters and their potential energy surface is exceptionally intricate. Searching for the global optimal structure (the lowest energy) among these isomers poses a significant challenge. Currently, modelling cluster energy predictions with traditional machine learning methods has several issues, including reliance on manual expertise, slow computation, heavy computational resource demands, and less efficient parameter tuning. RESULTS This paper introduces a predictive model for the energy of a gold cluster comprising twenty atoms (referred to as Au20 cluster). The model integrates the Multiple Strategy Fusion Whale Optimization Algorithm (MSFWOA) with the Light Gradient Boosting Machine (LightGBM), resulting in the MSFWOA-LightGBM model. This model employs the Coulomb matrix representation and eigenvalue solution methods for feature extraction. Additionally, it incorporates the Tent chaotic mapping, cosine convergence factor, and inertia weight updating strategy to optimize the Whale Optimization Algorithm (WOA), leading to the development of MSFWOA. Subsequently, MSFWOA is employed to optimize the parameters of LightGBM for supporting the energy prediction of Au20 cluster. CONCLUSIONS The experimental results show that the most stable Au20 cluster structure is a regular tetrahedron with the lowest energy, displaying tight and uniform atom distribution, high geometric symmetry. Compared to other models, the MSFWOA-LightGBM model excels in accuracy and correlation, with MSE, RMSE, and R2 values of 0.897, 0.947, and 0.879, respectively. Additionally, the MSFWOA-LightGBM model possesses outstanding scalability, offering valuable insights for material design, energy storage, sensing technology, and biomedical imaging, with the potential to drive research and development in these areas.
Collapse
Affiliation(s)
- Wu Wei
- School of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China
| | - Li Mengshan
- School of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China.
| | - Wu Yan
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, 341000, Jiangxi, China
| | - Guan Lixin
- School of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China
| |
Collapse
|
10
|
Wu Z, Lai J, Huang Q, Lin L, Lin S, Chen X, Huang Y. Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis. Front Neurosci 2023; 17:1285904. [PMID: 38156272 PMCID: PMC10753007 DOI: 10.3389/fnins.2023.1285904] [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: 08/30/2023] [Accepted: 10/30/2023] [Indexed: 12/30/2023] Open
Abstract
Background and objective Predicting mortality from traumatic brain injury facilitates early data-driven treatment decisions. Machine learning has predicted mortality from traumatic brain injury in a growing number of studies, and the aim of this study was to conduct a meta-analysis of machine learning models in predicting mortality from traumatic brain injury. Methods This systematic review and meta-analysis included searches of PubMed, Web of Science and Embase from inception to June 2023, supplemented by manual searches of study references and review articles. Data were analyzed using Stata 16.0 software. This study is registered with PROSPERO (CRD2023440875). Results A total of 14 studies were included. The studies showed significant differences in the overall sample, model type and model validation. Predictive models performed well with a pooled AUC of 0.90 (95% CI: 0.87 to 0.92). Conclusion Overall, this study highlights the excellent predictive capabilities of machine learning models in determining mortality following traumatic brain injury. However, it is important to note that the optimal machine learning modeling approach has not yet been identified. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=440875, identifier CRD2023440875.
Collapse
Affiliation(s)
- Zhe Wu
- Department of Neuronal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Jinqing Lai
- Department of Neuronal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Qiaomei Huang
- Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Long Lin
- Department of Neurosurgery, Fuzong Clinical Medical College, Fuzhou, Fujian, China
| | - Shu Lin
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Xiangrong Chen
- Department of Neuronal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Yinqiong Huang
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
- Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| |
Collapse
|
11
|
Manet R, Joubert C, Balanca B, Taverna XJ, Monneuse O, David JS, Dagain A. Neuro damage control: current concept and civilian applications. Neurochirurgie 2023; 69:101505. [PMID: 37806039 DOI: 10.1016/j.neuchi.2023.101505] [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: 06/19/2023] [Revised: 08/26/2023] [Accepted: 09/26/2023] [Indexed: 10/10/2023]
Abstract
Damage control (DC) initially referred to abbreviated (<1 h) surgical procedures to control abdominal hemorrhage in severe trauma patients, to avoid the 'bloody vicious circle' of hypothermia-coagulopathy-acidosis-hypocalcemia. Progressively, the concept was extended to pre-hospital and peri-operative surgical and non-surgical trauma care. The DC strategy can be applied either in a single severe trauma patient at risk of progression toward the bloody vicious circle or in case of limited or overwhelmed health resources (deprived environment, mass casualties, etc.). DC strategies in neurological casualties have improved over the last decade in military neurosurgeons, but remain poorly codified in civilian settings. In this comprehensive review, we summarize the current concept of neuro-DC, which includes surgical and medical care for neurological injuries as part of a DC strategy. Neuro-DC basically consists in: (i) preventing secondary brain injury; (ii) controlling intracranial bleeding; (iii) controlling intracranial pressure; (iv) limiting contamination of compound wounds; and (v) achieving secondary anatomical restoration.
Collapse
Affiliation(s)
- Romain Manet
- Service de Neurochirurgie B, Hôpital Neurologique Wertheimer, Hospices Civils de Lyon, Lyon, France.
| | - Christophe Joubert
- Service de Neurochirurgie, Hôpital d'Instruction des Armées St Anne, Toulon, France
| | - Baptiste Balanca
- Service de Neuro-Réanimation, Hôpital Neurologique Wertheimer, Hospices Civils de Lyon, Lyon, France
| | - Xavier-Jean Taverna
- Service de Réanimation Chirurgicale, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Olivier Monneuse
- Service de Chirurgie d'Urgence, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Jean-Stéphane David
- Service de Réanimation, Hôpital Lyon Sud, Hospices Civils de Lyon, Lyon, France
| | - Arnaud Dagain
- Service de Neurochirurgie, Hôpital d'Instruction des Armées St Anne, Toulon, France
| |
Collapse
|
12
|
Halvorson BD, Bao Y, Ward AD, Goldman D, Frisbee JC. Regulation of Skeletal Muscle Resistance Arteriolar Tone: Integration of Multiple Mechanisms. J Vasc Res 2023; 60:245-272. [PMID: 37769627 DOI: 10.1159/000533316] [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: 01/20/2023] [Accepted: 07/27/2023] [Indexed: 10/03/2023] Open
Abstract
INTRODUCTION Physiological system complexity represents an imposing challenge to gaining insight into how arteriolar behavior emerges. Further, mechanistic complexity in arteriolar tone regulation requires that a systematic determination of how these processes interact to alter vascular diameter be undertaken. METHODS The present study evaluated the reactivity of ex vivo proximal and in situ distal resistance arterioles in skeletal muscle with challenges across the full range of multiple physiologically relevant stimuli and determined the stability of responses over progressive alterations to each other parameter. The five parameters chosen for examination were (1) metabolism (adenosine concentration), (2) adrenergic activation (norepinephrine concentration), (3) myogenic activation (intravascular pressure), (4) oxygen (superfusate PO2), and (5) wall shear rate (altered intraluminal flow). Vasomotor tone of both arteriole groups following challenge with individual parameters was determined; subsequently, responses were determined following all two- and three-parameter combinations to gain deeper insight into how stimuli integrate to change arteriolar tone. A hierarchical ranking of stimulus significance for establishing arteriolar tone was performed using mathematical and statistical analyses in conjunction with machine learning methods. RESULTS Results were consistent across methods and indicated that metabolic and adrenergic influences were most robust and stable across all conditions. While the other parameters individually impact arteriolar tone, their impact can be readily overridden by the two dominant contributors. CONCLUSION These data suggest that mechanisms regulating arteriolar tone are strongly affected by acute changes to the local environment and that ongoing investigation into how microvessels integrate stimuli regulating tone will provide a more thorough understanding of arteriolar behavior emergence across physiological and pathological states.
Collapse
Affiliation(s)
- Brayden D Halvorson
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
| | - Yuki Bao
- Department of Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
| | - Aaron D Ward
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Daniel Goldman
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
- Department of Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
| | - Jefferson C Frisbee
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
| |
Collapse
|
13
|
Tu KC, Tau ENT, Chen NC, Chang MC, Yu TC, Wang CC, Liu CF, Kuo CL. Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury. Diagnostics (Basel) 2023; 13:3016. [PMID: 37761383 PMCID: PMC10528289 DOI: 10.3390/diagnostics13183016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established. METHOD Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test. RESULT The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores. CONCLUSION Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members.
Collapse
Affiliation(s)
- Kuan-Chi Tu
- Department of Neurosurgery, Chi Mei Medical Center, Tainan 710402, Taiwan; (K.-C.T.); (C.-C.W.)
| | - Eric nyam tee Tau
- Department of Neurosurgery, Chi Mei Medical Center, Tainan 710402, Taiwan; (K.-C.T.); (C.-C.W.)
| | - Nai-Ching Chen
- Department of Nursing, Chi Mei Medical Center, Tainan 710402, Taiwan; (N.-C.C.); (M.-C.C.); (T.-C.Y.)
| | - Ming-Chuan Chang
- Department of Nursing, Chi Mei Medical Center, Tainan 710402, Taiwan; (N.-C.C.); (M.-C.C.); (T.-C.Y.)
| | - Tzu-Chieh Yu
- Department of Nursing, Chi Mei Medical Center, Tainan 710402, Taiwan; (N.-C.C.); (M.-C.C.); (T.-C.Y.)
| | - Che-Chuan Wang
- Department of Neurosurgery, Chi Mei Medical Center, Tainan 710402, Taiwan; (K.-C.T.); (C.-C.W.)
- Center for General Education, Southern Taiwan University of Science and Technology, Tainan 710402, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan;
| | - Ching-Lung Kuo
- Department of Neurosurgery, Chi Mei Medical Center, Tainan 710402, Taiwan; (K.-C.T.); (C.-C.W.)
- Center for General Education, Southern Taiwan University of Science and Technology, Tainan 710402, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| |
Collapse
|
14
|
Yang S, Cao L, Zhou Y, Hu C. A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database. J Multidiscip Healthc 2023; 16:2625-2640. [PMID: 37701177 PMCID: PMC10493110 DOI: 10.2147/jmdh.s416943] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/29/2023] [Indexed: 09/14/2023] Open
Abstract
Objective The aim of this study was to develop and validate a machine learning-based predictive model that predicts 90-day mortality in ICU trauma patients. Methods Data of patients with severe trauma were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The performances of mortality prediction models generated using nine machine learning extreme gradient boosting (XGBoost), logistic regression, random forest, AdaBoost, multilayer perceptron (MLP) neural networks, support vector machine (SVM), light gradient boosting machine (GBM), k nearest neighbors (KNN) and gaussian naive bayes (GNB). The performance of the model was evaluated in terms of discrimination, calibration and clinical application. Results We found that the accuracy, sensitivity, specificity, PPV, NPV and F1 score of our proposed XGBoost model were 82.8%, 79.7%, 77.6%, 51.2%, 91.5% and 0.624, respectively. Among the nine models, the XGBoost model performed best. Compared with traditional logistic regression, the calibration curves of the XGBoost model and decision curve analysis (DCA) performed well. Conclusion Our study shows that the XGBoost model outperforms other machine learning models in predicting 90-day mortality in trauma patients. It can be used to assist clinicians in the early identification of mortality risk factors and early intervention to reduce mortality.
Collapse
Affiliation(s)
- Shan Yang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China
| | - Lirui Cao
- West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China
| | - Yongfang Zhou
- Department of Respiratory Care, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China
| | - Chenggong Hu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China
| |
Collapse
|
15
|
Trakulpanitkit A, Tunthanathip T. Comparison of intracranial pressure prediction in hydrocephalus patients among linear, non-linear, and machine learning regression models in Thailand. Acute Crit Care 2023; 38:362-370. [PMID: 37652865 PMCID: PMC10497900 DOI: 10.4266/acc.2023.00094] [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: 01/17/2023] [Revised: 04/23/2023] [Accepted: 06/20/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Hydrocephalus (HCP) is one of the most significant concerns in neurosurgical patients because it can cause increased intracranial pressure (ICP), resulting in mortality and morbidity. To date, machine learning (ML) has been helpful in predicting continuous outcomes. The primary objective of the present study was to identify the factors correlated with ICP, while the secondary objective was to compare the predictive performances among linear, non-linear, and ML regression models for ICP prediction. METHODS A total of 412 patients with various types of HCP who had undergone ventriculostomy was retrospectively included in the present study, and intraoperative ICP was recorded following ventricular catheter insertion. Several clinical factors and imaging parameters were analyzed for the relationship with ICP by linear correlation. The predictive performance of ICP was compared among linear, non-linear, and ML regression models. RESULTS Optic nerve sheath diameter (ONSD) had a moderately positive correlation with ICP (r=0.530, P<0.001), while several ventricular indexes were not statistically significant in correlation with ICP. For prediction of ICP, random forest (RF) and extreme gradient boosting (XGBoost) algorithms had low mean absolute error and root mean square error values and high R2 values compared to linear and non-linear regression when the predictive model included ONSD and ventricular indexes. CONCLUSIONS The XGBoost and RF algorithms are advantageous for predicting preoperative ICP and establishing prognoses for HCP patients. Furthermore, ML-based prediction could be used as a non-invasive method.
Collapse
Affiliation(s)
- Avika Trakulpanitkit
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
| | - Thara Tunthanathip
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
| |
Collapse
|
16
|
Zhang Z, Wang SJ, Chen K, Yin AA, Lin W, He YL. Machine learning algorithms for improved prediction of in-hospital outcomes after moderate-to-severe traumatic brain injury: a Chinese retrospective cohort study. Acta Neurochir (Wien) 2023; 165:2237-2247. [PMID: 37382689 DOI: 10.1007/s00701-023-05647-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/22/2023] [Indexed: 06/30/2023]
Abstract
AIM Controversy remains high over the superiority of advanced machine learning (ML) algorithms to conventional logistic regression (LR) in the prediction of prognosis after traumatic brain injury (TBI). This study aimed to compare the performance of ML and LR models in predicting in-hospital prognosis after TBI. METHOD In a single-center retrospective cohort of adult patients hospitalized for moderate-to-severe TBI (Glasgow coma score ≤12) in our hospital from 2011 to 2020, LR and three ML algorithms (XGboost, lightGBM, and FT-transformer) were run to build prediction models for in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes using either all 19 clinical and laboratory features or the 10 non-laboratory ones collected at admission to the neurological intensive care unit. The Shapley (SHAP) value was used for model interpretation. RESULT In total, 482 patients had an in-hospital mortality rate of 11.0%. A total of 23.0% of the patients had good functional scores (GOS ≥ 4) at discharge. All ML models performed better than the LR model in predicting in-hospital prognosis after TBI, among which the lightGBM model showed the best performance: When predicting mortality, the lightGBM model yielded an area under the curve (AUC) of 0.953 using all 19 features (the LR model: 0.813) and an AUC of 0.935 using 10 non-laboratory features (the LR model: 0.803); when predicting GOS functional outcomes, it yielded an AUC of 0.913 using all 19 features (the LR model: 0.832) and an AUC of 0.889 using non-laboratory data (the LR model: 0.818). The SHAP method identified key contributors to explain the lightGBM models. Finally, the integration of the lightGBM models with different prediction purposes was found to provide refined prognostic information, particularly for patients who survived moderate-to-severe TBI. CONCLUSION The study supported the superiority of ML to LR in predicting prognosis after moderate-to-severe TBI and highlighted its potential use for clinical application.
Collapse
Affiliation(s)
- Zan Zhang
- School of Electronic and Control Engineering, Chang'an University, Xi'an, 710064, China
| | - Sheng-Ju Wang
- School of Electronic and Control Engineering, Chang'an University, Xi'an, 710064, China
| | - Kun Chen
- Department of Anatomy, Histology and Embryology and K.K. Leung Brain Research Centre, School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China
- Shaanxi Provincial Key Laboratory of Clinic Genetics, Fourth Military Medical University, Xi'an, 710032, China
| | - An-An Yin
- Department of Neurosurgery, Xijing Institute of Clinical Neuroscience, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
- Department of Plastic and Reconstructive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
- Department of Biochemistry and Molecular Biology, Fourth Military Medical University, Xi'an, 710032, China.
| | - Wei Lin
- Department of Neurosurgery, Xijing Institute of Clinical Neuroscience, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
| | - Ya-Long He
- Department of Neurosurgery, Xijing Institute of Clinical Neuroscience, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
| |
Collapse
|
17
|
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
|
18
|
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
|
19
|
Song X, Li H, Chen Q, Zhang T, Huang G, Zou L, Du D. Predicting pneumonia during hospitalization in flail chest patients using machine learning approaches. Front Surg 2023; 9:1060691. [PMID: 36684357 PMCID: PMC9852626 DOI: 10.3389/fsurg.2022.1060691] [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: 10/03/2022] [Accepted: 11/14/2022] [Indexed: 01/07/2023] Open
Abstract
Objective Pneumonia is a common pulmonary complication of flail chest, causing high morbidity and mortality rates in affected patients. The existing methods for identifying pneumonia have low accuracy, and their use may delay antimicrobial therapy. However, machine learning can be combined with electronic medical record systems to identify information and assist in quick clinical decision-making. Our study aimed to develop a novel machine-learning model to predict pneumonia risk in flail chest patients. Methods From January 2011 to December 2021, the electronic medical records of 169 adult patients with flail chest at a tertiary teaching hospital in an urban level I Trauma Centre in Chongqing were retrospectively analysed. Then, the patients were randomly divided into training and test sets at a ratio of 7:3. Using the Fisher score, the best subset of variables was chosen. The performance of the seven models was evaluated by computing the area under the receiver operating characteristic curve (AUC). The output of the XGBoost model was shown using the Shapley Additive exPlanation (SHAP) method. Results Of 802 multiple rib fracture patients, 169 flail chest patients were eventually included, and 86 (50.80%) were diagnosed with pneumonia. The XGBoost model performed the best among all seven machine-learning models. The AUC of the XGBoost model was 0.895 (sensitivity: 84.3%; specificity: 80.0%).Pneumonia in flail chest patients was associated with several features: systolic blood pressure, pH value, blood transfusion, and ISS. Conclusion Our study demonstrated that the XGBoost model with 32 variables had high reliability in assessing risk indicators of pneumonia in flail chest patients. The SHAP method can identify vital pneumonia risk factors, making the XGBoost model's output clinically meaningful.
Collapse
Affiliation(s)
- Xiaolin Song
- School of Medicine, Chongqing University, Chongqing, China,Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Hui Li
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Qingsong Chen
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Tao Zhang
- School of Medicine, Chongqing University, Chongqing, China,Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Guangbin Huang
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Lingyun Zou
- Clinical Data Research Center, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China,Correspondence: Dingyuan Du Lingyun Zou
| | - Dingyuan Du
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China,Correspondence: Dingyuan Du Lingyun Zou
| |
Collapse
|
20
|
Deng Y, Liu S, Wang Z, Wang Y, Jiang Y, Liu B. Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients. Front Med (Lausanne) 2022; 9:933037. [PMID: 36250092 PMCID: PMC9554013 DOI: 10.3389/fmed.2022.933037] [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: 04/30/2022] [Accepted: 09/01/2022] [Indexed: 11/14/2022] Open
Abstract
Background In-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes. Materials and methods A retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms. Results A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission. Conclusion The prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.
Collapse
Affiliation(s)
- Yuhan Deng
- School of Public Health, Peking University, Beijing, China
| | - Shuang Liu
- School of Public Health, Peking University, Beijing, China
| | - Ziyao Wang
- School of Public Health, Peking University, Beijing, China
| | - Yuxin Wang
- School of Public Health, Peking University, Beijing, China
| | - Yong Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Yong Jiang,
| | - Baohua Liu
- School of Public Health, Peking University, Beijing, China
- *Correspondence: Baohua Liu,
| |
Collapse
|
21
|
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
|