پیش بینی نتایج سکته مغزی
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پیش بینی نتایج سکته مغزی

عنوان فارسی مقاله: ارزیابی روشهای یادگیری ماشین برای پیش بینی نتایج سکته مغزی با استفاده از ثبت بیماریهای کشوری
عنوان انگلیسی مقاله: Evaluation of Machine Learning Methods to Stroke Outcome Prediction Using a Nationwide Disease Registry
مجله/کنفرانس: روش ها و برنامه های رایانه ای در زیست پزشکی – Computer Methods and Programs in Biomedicine
رشته های تحصیلی مرتبط: پزشکی، مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مغز و اعصاب، هوش مصنوعی
کلمات کلیدی فارسی: نتایج سکته مغزی، یادگیری ماشین، سکته مغزی ایسکمیک، سکته مغزی ناشی از خونریزی
کلمات کلیدی انگلیسی: stroke outcome; machine learning; ischemic stroke; hemorrhagic stroke
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.cmpb.2020.105381
دانشگاه: Center for Information Technology, National Institutes of Health, Bethesda, Maryland, United States
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 4.256 در سال 2019
شاخص H_index: 83 در سال 2020
شاخص SJR: 0.753 در سال 2019
شناسه ISSN: ۰۱۶۹-۲۶۰۷
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 37
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14628
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


Graphical abstracts


۱٫ Introduction


۲٫ Materials and methods


۳٫ Results


۴٫ Discussion


۵٫ Conclusion


Contributors


Declaration of Competing Interest


Acknowledgements


Appendix C. Supplementary materials


Appendix A. List of Taiwan Stroke Registry Investigators


Appendix B. Details of Machine learning models


Appendix C


Research Data


Reference

نمونه متن انگلیسی مقاله

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 aftercare 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.


Introduction


Stroke is the second leading cause of mortality in the world and the leading adult disability in developed countries [1, 2]. Many stroke survivors are left with various neurological deficits resulting in impaired quality of life of variable extent that has been a significant burden on patients, caregivers, and society [3]. More precise prediction of functional outcomes after a stroke may help clinicians in developing an appropriate long-term management plan. For example, plans based on better prediction of the extent of recovery with appropriate rehabilitative measures with patients’ domestic condition taken into consideration for reaching shared decisions with patients and family members [4-6]. Much effort has been devoted to determining predictors of functional outcome after stroke [6-8]. Several medical communities have created scores that can predict the patient’s functional outcome using data readily available at admission [9-11]. These scores use statistical analysis to identify the most relevant covariates from a set of pre-selected factors by domain experts. Recently, machine learning has become ubiquitous for solving complex problems in many scientific domains, especially in medical diagnosis or prognosis prediction [12, 13].

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