ArticleNature communications2023
Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 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
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- New to cohort, not new to care: leveraging historical data in HIV clinical cohorts in the United States.American journal of epidemiology · 2026Article
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Benchmarking large language models for de-identification of electronic health record notes.BMJ health & care informatics · 2026Article
- Generating synthetic multi-national longitudinal cohorts for clinically grounded HIV research.Nature communications · 2026Article
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.Journal of healthcare informatics research · 2026Review
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Foundation Model for Capturing Complexity of Menstrual Health Data.npj women's health · 2026Article
- A case study comparing anonymized and synthetic health insurance claims data for medication safety assessments.NPJ digital medicine · 2026Article
- Ten Healthcare Delivery Trends and Their Measurement and Methodological Implications for Cancer Health Services Research.Health services research · 2026Article
- Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review.Journal of healthcare informatics research · 2026Review
- A novel pipeline for realistic synthetic longitudinal EHR data generation.Research square · 2026Article
- Generating Synthetic Multi-national Longitudinal Cohorts for Clinically Grounded HIV Research.medRxiv : the preprint server for health sciences · 2025Article
- Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer.JCO clinical cancer informatics · 2025Article
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- QAMT: An LLM-Based Framework for Quality-Assured Medical Time-Series Data Generation.Sensors (Basel, Switzerland) · 2025Article
- An Informatics Approach to Characterizing Rarely Documented Clinical Information in Electronic Health Records: Spiritual Care as an Exemplar.Applied clinical informatics · 2025Article
- A perspective for adapting generalist AI to specialized medical AI applications and their challenges.NPJ digital medicine · 2025Review
Corrections and comments
- Erratum issued
- Update of
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However, generating high-fidelity EHR data in its original, high-dimensional form poses challenges for existing methods. We propose Hierarchical Autoregressive Language mOdel (HALO) for generating longitudinal, high-dimensional EHR, which preserve the statistical properties of real EHRs and can train accurate ML models without privacy concerns. HALO generates a probability density function over medical codes, clinical visits, and patient records, allowing for generating realistic EHR data without requiring variable selection or aggregation. Extensive experiments demonstrated that HALO can generate high-fidelity data with high-dimensional disease code probabilities closely mirroring (above 0.9 R
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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.