ArticleJournal of the American Medical Informatics Association : JAMIA2020
Generating sequential electronic health records using dual adversarial autoencoder.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Synthetic data generation methods for longitudinal and time series health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer.JCO clinical cancer informatics · 2025Article
- Reimagining Evidence: Artificial Intelligence Synthetic Data Generation for Cancer Research.JCO clinical cancer informatics · 2025Article
- Essential Concepts in Artificial Intelligence: A Guide for Pediatric Providers.Children (Basel, Switzerland) · 2025Review
- Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study.JMIR medical informatics · 2025Article
- Generating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A review on generative AI models for synthetic medical text, time series, and longitudinal data.NPJ digital medicine · 2025Article
- Engineering novel features for diabetes complication prediction using synthetic electronic health records.Frontiers in genetics · 2025Article
- On the evaluation of synthetic longitudinal electronic health records.BMC medical research methodology · 2024Article
- Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges.Journal of medical Internet research · 2024Article
- IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2024Article
- SeqTrial: Utility Preserving Sequential Clinical Trial Data Generator.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Enabling Health Data Sharing with Fine-Grained Privacy.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2023Article
- Improving Diagnostics with Deep Forest Applied to Electronic Health Records.Sensors (Basel, Switzerland) · 2023Article
- Synthesizing Electronic Health Records for Predictive Models in Low-Middle-Income Countries (LMICs).Biomedicines · 2023Article
- Synthetic Tabular Data Evaluation in the Health Domain Covering Resemblance, Utility, and Privacy Dimensions.Methods of information in medicine · 2023Article
- Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications.NPJ digital medicine · 2023Article
- PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveRecent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients' independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder. MATERIALS AND
methodsWe propose a dual adversarial autoencoder (DAAE), which learns set-valued sequences of medical entities, by combining a recurrent autoencoder with 2 generative adversarial networks (GANs). DAAE improves the mode coverage and quality of generated sequences by adversarially learning both the continuous latent distribution and the discrete data distribution. Using the MIMIC-III (Medical Information Mart for Intensive Care-III) and UT Physicians clinical databases, we evaluated the performances of DAAE in terms of predictive modeling, plausibility, and privacy preservation.
resultsOur generated sequences of EHRs showed the comparable performances to real data for a predictive modeling task, and achieved the best score in plausibility evaluation conducted by medical experts among all baseline models. In addition, differentially private optimization of our model enables to generate synthetic sequences without increasing the privacy leakage of patients' data.
conclusionsDAAE can effectively synthesize sequential EHRs by addressing its main challenges: the synthetic records should be realistic enough not to be distinguished from the real records, and they should cover all the training patients to reproduce the performance of specific downstream tasks.
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