Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry

Ching Heng Lin, Kai Cheng Hsu, Kory R. Johnson, Yang C. Fann, Chon Haw Tsai, Yu Sun, Li Ming Lien, Wei Lun Chang, Po Lin Chen, Cheng Li Lin, Chung Y. Hsu

研究成果: 雜誌貢獻文章同行評審

14 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號105381
期刊Computer Methods and Programs in Biomedicine
190
DOIs
出版狀態已發佈 - 七月 2020

ASJC Scopus subject areas

  • 軟體
  • 電腦科學應用
  • 健康資訊學

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