SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking
2026-07-10 • Machine Learning
Machine Learning
AI summaryⓘ
The authors created SYNRARE, a tool that makes fake medical records to help test computer programs aimed at identifying rare diseases. Since real health data is hard to use due to privacy rules, SYNRARE lets researchers make synthetic records of patients who are similar to common patients but slightly different, mimicking rare diseases. This way, scientists can safely and quickly check how well their algorithms work under different conditions. The tool is easy to use and based on an existing framework called Synthea.
Rare DiseaseSynthetic Data GenerationElectronic Health RecordsMachine LearningSyntheaBenchmarkingPrivacyGraphical User InterfaceAlgorithm Testing
Authors
Nicolai Dinh Khang Truong, Richard Röttger
Abstract
Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants. Machine Learning Algorithms applied to Electronic Health Records show promise for accelerating the diagnosis; however, legal and privacy concerns pose significant barriers. To address these issues, Synthetic Data Generation is an alternative method for obtaining Electronic Health Records and can be applied with any Machine Learning algorithm for benchmarking and development purposes. Despite the availability of Synthetic Data Generation algorithms, support for generating a subset of patients that differ in a definable degree from the majority to simulate patients with RD is often lacking. Results: We present SYNRARE, a graphical user interface based on the Synthea framework that enables easier modification and generation of synthetic Electronic Health Records of RD patients, which differ only to a definable degree from patients with common diseases, thereby enabling the benchmarking and testing of algorithms under controlled technical conditions. SYNRARE enables researchers to rapidly benchmark their Machine Learning algorithms across any scenario. Availability and implementation: SYNRARE, including detailed instructions for installing, is available at https://gitlab.sdu.dk/screen4care/synrare.