ArticleJournal of biomedical informatics2019
Distributed learning from multiple EHR databases: Contextual embedding models for medical events.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Estimating covariate-balanced survival curve in distributed data environment using data collaboration quasi-experiment.Scientific reports · 2026Article
- Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications.NPJ digital medicine · 2026Article
- ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.Journal of biomedical informatics · 2025Article
- Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.Journal of the American Statistical Association · 2023Article
- Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study.Journal of medical Internet research · 2022Article
- SMART COVID Navigator, a Clinical Decision Support Tool for COVID-19 Treatment: Design and Development Study.Journal of medical Internet research · 2022Article
- Contrastive learning improves critical event prediction in COVID-19 patients.Patterns (New York, N.Y.) · 2021Article
- Differential privacy in health research: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Federated Learning for Healthcare Informatics.Journal of healthcare informatics research · 2021Article
- Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data.Scientific reports · 2020Article
- Patient Representation Transfer Learning from Clinical Notes based on Hierarchical Attention Network.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Electronic health record (EHR) data provide promising opportunities to explore personalized treatment regimes and to make clinical predictions. Compared with regular clinical data, EHR data are known for their irregularity and complexity. In addition, analyzing EHR data involves privacy issues and sharing such data is often infeasible among multiple research sites due to regulatory and other hurdles. A recently published work uses contextual embedding models and successfully builds one predictive model for more than seventy common diagnoses. Despite of the high predictive power, the model cannot be generalized to other institutions without sharing data. In this work, a novel method is proposed to learn from multiple databases and build predictive models based on Distributed Noise Contrastive Estimation (Distributed NCE). We use differential privacy to safeguard the intermediary information sharing. The numerical study with a real dataset demonstrates that the proposed method not only can build predictive models in a distributed manner with privacy protection, but also preserve model structure well and achieve comparable prediction accuracy. The proposed methods have been implemented as a stand-alone Python library and the implementation is available on Github (https://github.com/ziyili20/DistributedLearningPredictor) with installation instructions and use-cases.
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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.