ehr-phenotyping

Repository for the GA4GH phenopacket schema

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

How to: create SNOMED CT codelists

using AI model to infer patient phenotypes from identified named entities (instances of biomedical concepts)

Code accompanying our ACM CHIL paper: "TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records"

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

How to: find acute exacerbation of COPD events in UK primary care electronic healthcare records

Cast different EHR (electronic health record) layers to a shared latent space to identify patient subtypes

How to: create drug codelists for recorded prescriptions

RETIRED - Clinical phenotyping and ETL system

Lecture slides on Electronic Health Record Data Quality

Repositorio del proyecto "Detección automática de consultas para apoyar la atención de la salud sexual y reproductiva de personas con discapacidad"

Introduction to PERMIT project resources

This project shows a method with a topic modeling approach: non-negative matrix factorization (NMF), used in EHR for subphenotyping and identifying the relationship between phenotypes and genetic variants

A research framework for longitudinal EHR sequence modeling, exploring multiple temporal representations, learning objectives, and model classes for disease progression, survival analysis, and temporal phenotyping under censoring and irregular follow-up.

Quality and Outcomes Framework (QOF) notes

Synthetic EHR cancer cohort phenotyping and validation workflow using longitudinal healthcare data and Python analytics.

Diffusion-based representation learning for extracting meaningful patient embeddings from longitudinal EHR data.

PheMINE is a reproducible, automated workflow for analyzing electronic health records. It streamlines cohort construction, performs permutation-based phecode enrichment analysis, and trains supervised learning models (Random Forest, XGBoost, Neural Networks) for disease prediction.