cumc-dbmi/cehrbert
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks
This project helps healthcare researchers and data scientists use electronic health record (EHR) data to predict patient outcomes or understand disease progression. It takes structured EHR data, such as medical codes, diagnoses, and procedures, typically in OMOP or MEDS format, and generates specialized patient representations. These representations are then used to train and evaluate predictive models for various clinical tasks.
Use this if you are working with structured electronic health record (EHR) data and need to build predictive models that account for the chronological order and timing of patient visits and medical events.
Not ideal if your data is not in OMOP or MEDS format, or if you are working with unstructured clinical notes or images rather than structured medical codes.
Stars
45
Forks
18
Language
Python
License
MIT
Category
Last pushed
Dec 09, 2025
Commits (30d)
0
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