mims-harvard/ClinVec
ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in Medicine
This project offers a standardized way to represent medical terms like diagnoses, medications, and lab tests as 'embeddings,' which are numerical codes that capture their meanings and relationships. Researchers and clinicians can use these embeddings to analyze clinical data and develop AI tools without needing to access patient-level information. It provides a foundational resource for advancing precision medicine by understanding how different clinical concepts relate to each other.
Use this if you are a medical researcher or data scientist working with electronic health record (EHR) data and need a machine-readable, hypothesis-free way to understand relationships between clinical codes.
Not ideal if you need a tool for direct patient care or if your research specifically requires patient-level data for analysis.
Stars
83
Forks
12
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 22, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/mims-harvard/ClinVec"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
NYUMedML/DeepEHR
Chronic Disease Prediction Using Medical Notes
mims-harvard/SHEPHERD
SHEPHERD: Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
biocentral/biocentral_server
Compute functionality for biocentral.
nomic-ai/contrastors
Train Models Contrastively in Pytorch
hybrid-kg/clep
🤖 A Python Package for generating new patient representations driven by data and prior knowledge