eds-ner-cardioccc

EDS-NER-CARDIOCCC

This repository contains the final NER model trained on the CardioCCC dataset. CardioCCC is a collection of cardiology clinical case reports used for domain adaptation. Clinical case reports are a textual genre in medicine that describe a patient’s medical history, symptoms, diagnosis, and treatment in detail.

The model implementation is based on EDS-NLP, a library developed by the data science team of the Greater Paris University Hospitals (AP-HP) for clinical natural language processing.

The entities that are detected are listed below.

Quickstart

1. Install the latest version of edsnlp

   pip install "edsnlp[ml]" -U
   

2. Load the model

   import edsnlp
   
   nlp = edsnlp.load("Aremaki/eds-ner-cardioccc", auto_update=True)
   doc = nlp(
       "La paciente con diabetes mellitus "
       "presentó fiebre y se le realizó "
       "una radiografía antes de tomar metformina. "
   )
   
   for ent in doc.ents:
       print(ent, ent.label_)
   

To apply the model on many documents using one or more GPUs, refer to the documentation of edsnlp.

Metrics

Installation to reproduce

If you'd like to reproduce eds-ner-cardioccc's training or contribute to its development, you should first clone it:

git clone https://github.com/Aremaki/eds_ner_cardioccc.git
cd eds_ner_cardioccc

Acknowledgement

We would like to thank the Life science team at the Barcelona Supercomputing Center (BSC) who designed the CardioCCC dataset and trained the base model bsc-bio-ehr-es We would like to thank the data science team of the Greater Paris University Hospitals (AP-HP) who developped the EDS-NLP library.