ArticleScientific reports2022
Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Embracing the Future of Medical Education With Large Language Model-Based Virtual Patients: Scoping Review.Journal of medical Internet research · 2025Article
- Synthetic data and health equity: accounting for racism and sexism in health care delivery.Health affairs scholar · 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
- How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data.BMC medical informatics and decision making · 2025Article
- Enhancing the Functionalities of Personal Health Record Systems: Empirical Study Based on the HL7 Personal Health Record System Functional Model Release 1.JMIR medical informatics · 2024Article
- Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study.JMIR AI · 2024Article
- Benchmarking the symptom-checking capabilities of ChatGPT for a broad range of diseases.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Tools and frameworks for evaluating the implementation of learning health systems: a scoping review.Health research policy and systems · 2024Article
- Article
- Gathering speed and countering tensions in the rapid learning health system.Learning health systems · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When enabled by machine learning (ML), Learning Health Systems (LHS) hold promise for improving the effectiveness of healthcare delivery to patients. One major barrier to LHS research and development is the lack of access to EHR patient data. To overcome this challenge, this study demonstrated the feasibility of developing a simulated ML-enabled LHS using synthetic patient data. The ML-enabled LHS was initialized using a dataset of 30,000 synthetic Synthea patients and a risk prediction XGBoost base model for lung cancer. 4 additional datasets of 30,000 patients were generated and added to the previous updated dataset sequentially to simulate addition of new patients, resulting in datasets of 60,000, 90,000, 120,000 and 150,000 patients. New XGBoost models were built in each instance, and performance improved with data size increase, attaining 0.936 recall and 0.962 AUC (area under curve) in the 150,000 patients dataset. The effectiveness of the new ML-enabled LHS process was verified by implementing XGBoost models for stroke risk prediction on the same Synthea patient populations. By making the ML code and synthetic patient data publicly available for testing and training, this first synthetic LHS process paves the way for more researchers to start developing LHS with real patient data.
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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.