ArticleNPJ digital medicine2023
EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
29 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
- A Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study.JMIR AI · 2026Article
- An integrated evaluation framework for synthetic clinical data in severely imbalanced settings: fidelity, privacy-risk profiling, and diagnostic utility.BMC medical informatics and decision making · 2026Article
- A Foundation Model for Capturing Complexity of Menstrual Health Data.npj women's health · 2026Article
- An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.NAR genomics and bioinformatics · 2026Article
- Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review.Journal of healthcare informatics research · 2026Review
- Deep generative hidden Markov models for synthetic patient data generation: a novel approach for medical AI research.BMC medical informatics and decision making · 2026Article
- CardioEHR: A longitudinal electronic health record dataset of cardiovascular patients from central China.Scientific data · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Adaptive Evolution-Inspired Algorithm for Intrusion Detection in Bioinformatics Systems.Evolutionary bioinformatics online · 2026Article
- Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- The impact of artificial intelligence on biomarker discovery.Emerging topics in life sciences · 2025Article
- QAMT: An LLM-Based Framework for Quality-Assured Medical Time-Series Data Generation.Sensors (Basel, Switzerland) · 2025Article
- Synthetic data and health equity: accounting for racism and sexism in health care delivery.Health affairs scholar · 2025Article
- RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.Patterns (New York, N.Y.) · 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 data-efficient strategy for building high-performing medical foundation models.Nature biomedical engineering · 2025Article
- Synthetic Data for the Get With The Guidelines-Stroke Registry.Journal of the American Heart Association · 2025Article
- Preserving information while respecting privacy through an information theoretic framework for synthetic health data generation.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Privacy concerns often arise as the key bottleneck for the sharing of data between consumers and data holders, particularly for sensitive data such as Electronic Health Records (EHR). This impedes the application of data analytics and ML-based innovations with tremendous potential. One promising approach for such privacy concerns is to instead use synthetic data. We propose a generative modeling framework, EHR-Safe, for generating highly realistic and privacy-preserving synthetic EHR data. EHR-Safe is based on a two-stage model that consists of sequential encoder-decoder networks and generative adversarial networks. Our innovations focus on the key challenging aspects of real-world EHR data: heterogeneity, sparsity, coexistence of numerical and categorical features with distinct characteristics, and time-varying features with highly-varying sequence lengths. Under numerous evaluations, we demonstrate that the fidelity of EHR-Safe is almost-identical with real data (<3% accuracy difference for the models trained on them) while yielding almost-ideal performance in practical privacy metrics.
Identifiers
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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.