Ching-Heng Lin1, Kai-Cheng Hsu2, Kory R Johnson3, Yang C Fann4, Chon-Haw Tsai5, Yu Sun6, Li-Ming Lien7, Wei-Lun Chang8, Po-Lin Chen9, Cheng-Li Lin10, Chung Y Hsu11. 1. Center for Information Technology, National Institutes of Health, Bethesda, MD, United States; Bioinformatics Section, National Institute of Neurological Disorder and Stroke, National Institutes of Health, Bethesda, MD, United States. 2. Bioinformatics Section, National Institute of Neurological Disorder and Stroke, National Institutes of Health, Bethesda, MD, United States; Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan. 3. Bioinformatics Section, National Institute of Neurological Disorder and Stroke, National Institutes of Health, Bethesda, MD, United States. 4. Bioinformatics Section, National Institute of Neurological Disorder and Stroke, National Institutes of Health, Bethesda, MD, United States. Electronic address: fann@ninds.nih.gov. 5. Division of Nephrology, China Medical University Hospital, Taichung, Taiwan. 6. Neurology, En Chu Kong Hospital, New Taipei City, Taiwan. 7. Department of Neurology, Shin Kong Wu-Ho-Su Memorial Hospital, Taipei, Taiwan; Department of Neurology, College of Medicine, Taipei Medical University, Taipei, Taiwan. 8. Department of Neurology, Show Chwan Memorial Hospital, Changhua County, Taiwan. 9. Neurological Institute, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan. 10. Management Office for Health Data, China Medical University Hospital, Taichung, Taiwan. 11. Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.
Abstract
INTRODUCTION: Being able to predict functional outcomes after a stroke is highly desirable for clinicians. This allows clinicians to set reasonable goals with patients and relatives, and to reach shared after-care decisions for recovery or rehabilitation. The aim of this study was to apply various machine learning (ML) methods for 90-day stroke outcome predictions, using a nationwide disease registry. METHODS: This study used the Taiwan Stroke Registry (TSR) which has prospectively collected data from stroke patients since 2006. Three known ML models (support vector machine, random forest, and artificial neural network), and a hybrid artificial neural network were implemented and evaluated by 10-time repeated hold-out with 10-fold cross-validation. RESULTS: ML techniques present over 0.94 AUC in both ischemic and hemorrhagic stroke using preadmission and inpatient data. By adding follow-up data, the prediction ability improved to 0.97 AUC. We screened 206 clinical variables to identify 17 important features from the ischemic stroke dataset and 22 features from the hemorrhagic stroke dataset without losing much performance. Error analysis revealed that most prediction errors come from more severe stroke patients. CONCLUSION: The study showed that ML techniques trained from large, cross-reginal registry datasets were able to predict functional outcome after stroke with high accuracy. The follow-up data is important which can further improve the predictive models' performance. With similar performances among different ML techniques, the algorithm's characteristics and performance on severe stroke patients will be the primary focus when we further develop inference models and artificial intelligence tools for potential medical. Published by Elsevier B.V.
INTRODUCTION: Being able to predict functional outcomes after a stroke is highly desirable for clinicians. This allows clinicians to set reasonable goals with patients and relatives, and to reach shared after-care decisions for recovery or rehabilitation. The aim of this study was to apply various machine learning (ML) methods for 90-day stroke outcome predictions, using a nationwide disease registry. METHODS: This study used the Taiwan Stroke Registry (TSR) which has prospectively collected data from strokepatients since 2006. Three known ML models (support vector machine, random forest, and artificial neural network), and a hybrid artificial neural network were implemented and evaluated by 10-time repeated hold-out with 10-fold cross-validation. RESULTS: ML techniques present over 0.94 AUC in both ischemic and hemorrhagic stroke using preadmission and inpatient data. By adding follow-up data, the prediction ability improved to 0.97 AUC. We screened 206 clinical variables to identify 17 important features from the ischemic stroke dataset and 22 features from the hemorrhagic stroke dataset without losing much performance. Error analysis revealed that most prediction errors come from more severe strokepatients. CONCLUSION: The study showed that ML techniques trained from large, cross-reginal registry datasets were able to predict functional outcome after stroke with high accuracy. The follow-up data is important which can further improve the predictive models' performance. With similar performances among different ML techniques, the algorithm's characteristics and performance on severe strokepatients will be the primary focus when we further develop inference models and artificial intelligence tools for potential medical. Published by Elsevier B.V.
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