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

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

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

Electronic Health Record Analysis with Python.

Open Odonto Application

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

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

Convert arbitrary EHR extracts to FHIR.

Deep learning for cancer symptoms monitoring on the basis of EHR unstructured clinical notes

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

attribute-based access control implementation for EHRs

Natural language generation for discrete data in EHRs

🧪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

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

The OMOP Common Data Model(v6) implemented in Django (v3)

Continual Learning of Electronic Health Records (EHR).

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

Official repo for "Characterizing Stigmatizing Language in Medical Records" (ACL 2023)

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use …

Estimate sentiment in clinical notes via keywords or deep learning models