ArticleNPJ digital medicine2023
Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications.
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 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 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
- Generation of contrast-enhanced cardiac MRI from contrast-free scans: a multi-center, multi-manufacturer study.European radiology · 2026Article
- Conditional Perplexity Scoring for Large Language Model-Generated Differential Diagnoses in Case Reports: Preliminary Computational Evaluation.JMIR formative research · 2026Article
- TabularQGAN: a quantum generative model for tabular data synthesis.Scientific reports · 2026Article
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- A novel digital twin strategy to examine the implications of randomized clinical trials for real-world populations.NPJ digital medicine · 2026Article
- Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review.Journal of healthcare informatics research · 2026Review
- Safeguarding patient privacy in eHealth systems: Bridging theory and practice through a scoping review and healthcare survey.PLOS digital health · 2026Article
- Balancing Privacy and Utility in Child and Adolescent Mental Health Services Research: Retrospective Cohort Study on Synthetic Data Generation.JMIR medical informatics · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- A novel pipeline for realistic synthetic longitudinal EHR data generation.Research square · 2026Article
- Shaping the Future of AI in Organ Transplantation: Position Paper of the European Society for Organ Transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2026Article
- A deep state-space analysis framework for cancer patient latent state estimation and classification from EHR time-series data.PloS one · 2026Article
- Ten tips to harnessing generative AI for high-quality MCQS in medical education assessment.Medical education online · 2025Article
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer.JCO clinical cancer informatics · 2025Article
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
- Synthetic data and health equity: accounting for racism and sexism in health care delivery.Health affairs scholar · 2025Article
- Evaluating the utility of data integration with synthetic data and statistical matching.Scientific reports · 2025Article
- Equity-enhanced glaucoma progression prediction from OCT with knowledge distillation.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy.
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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.