Synthetic Patient Population Simulator

Open Synthea patient data for machine learning and team training.

More realistic synthetic medication data.

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

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

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

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

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.

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.

Synthetic health dataset generator

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.

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

Test Data Created Using Synthea

This repository highlights course work completed during Population Health Informatics course in Spring 2025. It is a comprehensive part of final project submission.

A dockerized healthcare data generator based on Synthea

The UnHealth Dashboard

docker image for generating synthetic clinical data using synthea

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