Synthetic Patient Population Simulator

Open Synthea patient data for machine learning and team training.

Generate realistic and complete synthetic test data for US Core v3.1.0

More realistic synthetic medication data.

A configurable synthetic patient generator which delivers and centralizes information on a repetitional continuous basis via the message broker technology and through a healthcare integration engine

Synthetic health dataset generator

The purpose of this application is to test LLM-generated interpretations of medical observations. The explanations are generated fully automatically by a large language model. This application should be used for experimental purposes only. It does not provide support for real world cases and does not replace advice from medical professionals.

A practical guide for a quick start in building learning health systems (LHS) units.

Examples of exploring synthetic healthcare data from the Agency for Healthcare Research and Quality in the United States Department of Health and Human Services, and MITRE Corporation.

Semantic web representation for the Synthea.

Create a simulation patient population using Synthea and ETL it into the OMOP Common Data Model

A deep learning library for Electronic Health Record (EHR) data

The Patient Pathway Extractor is an application to transform patient medical data into a compact machine processable representation that can be used for machine learning and deep learning tasks.

This healthcare analytics project uses SQL queries to extract insights from patient data, encounters data, and etc.

Test Data Created Using Synthea

The UnHealth Dashboard

Sample ML workflow in Argo

A dockerized healthcare data generator based on Synthea

A project that uses SQL to analyze health data from the Synthea dataset. It looks at common health conditions, resource use, and provides insights for better health management.

Evaluation of the PhenoMan API with synthetic FHIR data