当前位置： SCI文献检索 > COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE期刊下所有文献 > Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry.

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

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.

journal_name

Comput Methods Programs Biomed

journal_title

Computer methods and programs in biomedicine

authors

Lin CH,Hsu KC,Johnson KR,Fann YC,Tsai CH,Sun Y,Lien LM,Chang WL,Chen PL,Lin CL,Hsu CY,Taiwan Stroke Registry Investigators.

doi

10.1016/j.cmpb.2020.105381

subject

Has Abstract

pub_date

2020-07-01 00:00:00

pages

105381

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(19)31436-1

journal_volume

190

pub_type

杂志文章

在线工具

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