This paper is in the following e-collection/theme issue:

Foundations of AI (47) Applications of AI (104)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46840, first published .
Predictive Performance of Machine Learning–Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study

Predictive Performance of Machine Learning–Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study

Predictive Performance of Machine Learning–Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study

Authors of this article:

Fumi Irie1, 2 Author Orcid Image ;   Koutarou Matsumoto3 Author Orcid Image ;   Ryu Matsuo1, 2 Author Orcid Image ;   Yasunobu Nohara4 Author Orcid Image ;   Yoshinobu Wakisaka2 Author Orcid Image ;   Tetsuro Ago2, 5 Author Orcid Image ;   Naoki Nakashima6 Author Orcid Image ;   Takanari Kitazono2, 5 Author Orcid Image ;   Masahiro Kamouchi1, 5 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Bui H, Nguyễn Thị Phương Q, Cam Tu H, Nguyen Phuong S, Pham T, Vu T, Nguyen Thi Thu H, Khanh Ho L, Nguyen Tien D. The Roles of NOTCH3 p.R544C and Thrombophilia Genes in Vietnamese Patients With Ischemic Stroke: Study Involving a Hierarchical Cluster Analysis. JMIR Bioinformatics and Biotechnology 2024;5:e56884 View