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

Foundations of AI (57) Applications of AI (246) Decision Support for Health Professionals (1468) Electronic Health Records (1271) Machine Learning (1971) Ontologies, Classifications, and Coding (309) Clinical Informatics (1257)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62985, first published .
Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis

Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis

Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis

Authors of this article:

De Rong Loh1, 2 Author Orcid Image ;   Elliot D Hill2 Author Orcid Image ;   Nan Liu1 Author Orcid Image ;   Geraldine Dawson3 Author Orcid Image ;   Matthew M Engelhard2 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Su C, Hasebe M. Machine Learning Models for Predicting Acute and Chronic Kidney Diseases During the Post‐Covid‐19 Pandemic. Nephrology 2025;30(7) View