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

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Machine Learning (1385) Clinical Information and Decision Making (1328) Decision Support for Health Professionals (1137)

Published on in Vol 10, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38053, first published .
A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study

A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study

A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study

Authors of this article:

Xiangzhou Zhang1 Author Orcid Image ;   Yunfei Xue1, 2 Author Orcid Image ;   Xinyu Su1, 2 Author Orcid Image ;   Shaoyong Chen1, 2 Author Orcid Image ;   Kang Liu1, 3 Author Orcid Image ;   Weiqi Chen1 Author Orcid Image ;   Mei Liu4 Author Orcid Image ;   Yong Hu1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Chiasakul T, Lam B, McNichol M, Robertson W, Rosovsky R, Lake L, Vlachos I, Adamski A, Reyes N, Abe K, Zwicker J, Patell R. Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis. European Journal of Haematology 2023;111(6):951 View
  2. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View