Validation of the Machine Learning-Based Stroke Impact Scale With a Cross-Cultural Sample

SHIH-CHIEH LEEChou, Chia-YehChia-YehChouChen, Po-TingPo-TingChenWu, Tzu-YiTzu-YiWuI-PING HSUEHCHING-LIN HSIEH2024-02-202024-02-202024-03-010272-9490https://scholars.lib.ntu.edu.tw/handle/123456789/639819The machine learning-based Stroke Impact Scale (ML-SIS) is an efficient short-form measure that uses 28 items to provide domain scores comparable to those of the original 59-item Stroke Impact Scale-Third Edition (SIS 3.0). However, its utility is largely unknown because it has not been cross-validated with an independent sample.enValidation of the Machine Learning-Based Stroke Impact Scale With a Cross-Cultural Samplejournal article10.5014/ajot.2024.050356382716402-s2.0-85183495666https://api.elsevier.com/content/abstract/scopus_id/85183495666