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

Early Results from COVID-19 Studies (371) Machine Learning (2233) Methods and Instruments in Medical Informatics (213) Design and Usability of Clinical Software and EHRs (368)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65585, first published .
Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study

Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study

Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study

Authors of this article:

Tetsuo Ishikawa1, 2, 3, 4, 5, 6 Author Orcid Image ;   Masahiro Shinoda4 Author Orcid Image ;   Megumi Oya1, 3, 4, 6 Author Orcid Image ;   Koichi Ashizaki1, 4 Author Orcid Image ;   Shinichiro Ota4 Author Orcid Image ;   Kenichi Kamachi4 Author Orcid Image ;   Kazuhiro Sakurada1, 2 Author Orcid Image ;   Eiryo Kawakami1, 3, 4, 6 Author Orcid Image ;   Masaharu Shinkai4 Author Orcid Image
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Journals

  1. Aoki Y, Takeda H, Yokota K, Yoshida R. Temporal Reproducibility of a Genetic Algorithm–Derived Health Risk Score: A Standardized Out-of-Fold Validation Framework (2021–2023) (Preprint). JMIR Bioinformatics and Biotechnology 2025 View