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

Machine Learning (2225) Electronic Health Records (1376) Decision Support for Health Professionals (1633) Dementia and Cognitive Decline (739) Cardiac Risk and Cardiac Risk Calculators (118) Foundation Models and Their Applications in AI (57)

Published on in Vol 13 (2025)

This is a member publication of Bibsam Consortium

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68138, first published .
Trajectory-Ordered Objectives for Self-Supervised Representation Learning of Temporal Healthcare Data Using Transformers: Model Development and Evaluation Study

Trajectory-Ordered Objectives for Self-Supervised Representation Learning of Temporal Healthcare Data Using Transformers: Model Development and Evaluation Study

Trajectory-Ordered Objectives for Self-Supervised Representation Learning of Temporal Healthcare Data Using Transformers: Model Development and Evaluation Study

Authors of this article:

Ali Amirahmadi1 Author Orcid Image ;   Farzaneh Etminani2, 3 Author Orcid Image ;   Jonas Björk4 Author Orcid Image ;   Olle Melander5 Author Orcid Image ;   Mattias Ohlsson2, 6 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Amirahmadi A, Etminani F, Ohlsson M. Adaptive noise-augmented attention for enhancing Transformer fine-tuning on longitudinal medical data. Frontiers in Artificial Intelligence 2025;8 View
  2. Singh M, Betgeri S, Kakar S. Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention. Journal of Ovarian Research 2026;19(1) View
  3. Guo L, Arciniegas S, Yan A, Fries J, Tomlinson G, Sung L. Systematic review of foundation models for structured electronic health records. Journal of the American Medical Informatics Association 2026 View