دورية أكاديمية
Prognosticating global functional outcome in the recurrent ischemic stroke using baseline clinical and pre-clinical features: A machine learning study.
| العنوان: | Prognosticating global functional outcome in the recurrent ischemic stroke using baseline clinical and pre-clinical features: A machine learning study. |
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| المؤلفون: | Dao TNP; Faculty of Traditional Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam.; Can Tho Traditional Medicine Hospital, Can Tho, Vietnam., Dang HNT; Department of Cardiology, Hoan My Cuu Long General Hospital, Can Tho, Vietnam., Pham MTK; Department of Cardiac Surgery, Can Tho Central General Hospital, Can Tho, Vietnam., Nguyen HT; Department of Nutrition and Food Safety, Faculty of Public Health, Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam., Tran Chi C; Can Tho Stroke International Services (S.I.S) General Hospital, Can Tho, Vietnam., Le MV; Department of Neurology, Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam.; Department of Neurology, Can Tho University of Medicine and Pharmacy Hospital, Can Tho, Vietnam.; Department of Neurology, Can Tho Central General Hospital, Can Tho, Vietnam. |
| المصدر: | Journal of evaluation in clinical practice [J Eval Clin Pract] 2024 Jul 19. Date of Electronic Publication: 2024 Jul 19. |
| Publication Model: | Ahead of Print |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 9609066 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-2753 (Electronic) Linking ISSN: 13561294 NLM ISO Abbreviation: J Eval Clin Pract Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Oxford, England : Wiley-Blackwell, c1995- |
| مستخلص: | Background and Purpose: Recurrent ischemic stroke (RIS) induces additional functional limitations in patients. Prognosticating globally functional outcome (GFO) in RIS patients is thereby important to plan a suitable rehabilitation programme. This study sought to investigate the ability of baseline features for classifying the patients with and without improving GFO (task 1) and identifying patients with poor GFO (task 2) at the third month after discharging from RIS. Methods: A total of 86 RIS patients were recruited and divided into the training set and testing set (50:50). The clinical and pre-clinical data were recorded. The outcome was the changes in Modified Rankin Scale (mRS) (task 1) and the mRS score at the third month (mRS 0-2: good GFO, mRS >2: poor GFO) (task 2). The permutation importance ranking method selected features. Four algorithms were trained on the training set with five-fold cross-validation. The best model was tested on the testing set. Results: In task 1, the support vector machine (SVM) model outperformed the other models, with the high performance matrix on the training set (sensitivity = 0.80; specificity = 1.00) and the testing set (sensitivity = 0.80; specificity = 0.95). In task 2, the SVM model with selected features also performed well on both datasets (training set: sensitivity = 0.76; specificity = 0.92; testing set: sensitivity = 0.72; specificity = 0.88). Conclusion: A machine learning model could be used to classify GFO responses to treatment and identify the third-month poor GFO in RIS patients, supporting physicians in clinical practice. (© 2024 John Wiley & Sons Ltd.) |
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| فهرسة مساهمة: | Keywords: global functional outcome; machine learning; modified ranking scale; recurrent ischemic stroke |
| تواريخ الأحداث: | Date Created: 20240720 Latest Revision: 20240720 |
| رمز التحديث: | 20240721 |
| DOI: | 10.1111/jep.14100 |
| PMID: | 39031001 |
| قاعدة البيانات: | MEDLINE |
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