Open Source, Enterprise and Modern Health Information System

Electronic Health Record Analysis with Python.

Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"

The missing middleware layer in healthcare AI 💫 🏥

[EMNLP'24] EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

NER and Relation Extraction from Electronic Health Records (EHR).

🧪Yet Another ICU Benchmark: a holistic framework for the standardization of clinical prediction model experiments. Provide custom datasets, cohorts, prediction tasks, endpoints, preprocessing, and models. Paper: https://arxiv.org/abs/2306.05109

FHIR Python Analysis Client and Kit (FHIRPACK) is a general purpose FHIR client that simplifies the access, analysis and representation of FHIR and EHR data using PANDAS, an ETL philosophy and a functional syntax. It was initially developed at the IKIM and HDDBS in Germany. Read more at https://zenodo.org/record/8006589

A Comprehensive Benchmark For COVID-19 Predictive Modeling Using Electronic Health Records

An open source implementation of the paper: "Sequential Diagnosis with Language Models" From Microsoft Built with Swarms Framework

Code for the paper: Multi-Label Clinical Time-Series Generation via Conditional GAN (IEEE TKDE)

A Multimodal Transformer: Fusing Clinical Notes With Structured EHR Data for Interpretable In-Hospital Mortality Prediction

[EMNLP'24] MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning

FHIR MCP Server for handling medical data standard.

Fact Verification for Clinical Notes with LLMs

HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research

[ML4H 2022] This is the code for our paper `Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR'.

COVID-19 EHR data analysis pipeline

Natural language generation for discrete data in EHRs