ArticleJournal of biomedical informatics2022
AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences.
Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- What affects natural killer cell activity: a cross-sectional study.Frontiers in immunology · 2026Article
- A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data.NPJ digital medicine · 2025Article
- Trajectory-Ordered Objectives for Self-Supervised Representation Learning of Temporal Healthcare Data Using Transformers: Model Development and Evaluation Study.JMIR medical informatics · 2025Article
- Out-of-distribution reject option method for dataset shift problem in early disease onset prediction.Scientific reports · 2025Article
- Predicting Partially Observed Long-Term Outcomes with Adversarial Positive-Unlabeled Domain Adaptation.Proceedings of machine learning research · 2025Article
- Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review.JMIR medical informatics · 2024Article
- Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic.BMC medical informatics and decision making · 2024Review
- Stable clinical risk prediction against distribution shift in electronic health records.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Early detection of heart failure (HF) can provide patients with the opportunity for more timely intervention and better disease management, as well as efficient use of healthcare resources. Recent machine learning (ML) methods have shown promising performance on diagnostic prediction using temporal sequences from electronic health records (EHRs). In practice, however, these models may not generalize to other populations due to dataset shift. Shifts in datasets can be attributed to a range of factors such as variations in demographics, data management methods, and healthcare delivery patterns. In this paper, we use unsupervised adversarial domain adaptation methods to adaptively reduce the impact of dataset shift on cross-institutional transfer performance. The proposed framework is validated on a next-visit HF onset prediction task using a BERT-style Transformer-based language model pre-trained with a masked language modeling (MLM) task. Our model empirically demonstrates superior prediction performance relative to non-adversarial baselines in both transfer directions on two different clinical event sequence data sources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.