PKG-INFO

Metadata-Version: 2.3
Name: eds-ner-cardioccc
Version: 0.1.0
Summary: EDS-NER-CARDIOCCC is NER model for detecting medication, procedure, disease and symptom in cardiology clinical reports
Author-email: Adam Remaki <ad.remaki@gmail.com>
License-File: LICENSE
Requires-Python: <4.0,>=3.9.1
Requires-Dist: edsnlp[ml]>=0.16.0
Requires-Dist: sentencepiece>=0.1.96
Description-Content-Type: text/markdown

---
language:
- es
pipeline_tag: token-classification
tags:
- biomedical
- ner
- clinical
- ehr
- cardiology
- nlp
- edsnlp
- caradioccc
license: apache-2.0
library_name: edsnlp
model-index:
- name: Aremaki/eds-ner-cardioccc
  results:
  - task:
      type: token-classification
    dataset:
      name: CardioCCC
      type: public
    metrics:
    - type: precision
      name: Token Scores / MEDICATION / Precision
      value: 0.93
    - type: recall
      name: Token Scores / MEDICATION / Recall
      value: 0.94
    - type: f1
      name: Token Scores / MEDICATION / F1
      value: 0.93
    - type: precision
      name: Token Scores / PROCEDURE / Precision
      value: 0.85
    - type: recall
      name: Token Scores / PROCEDURE / Recall
      value: 0.85
    - type: f1
      name: Token Scores / PROCEDURE / F1
      value: 0.85
    - type: precision
      name: Token Scores / DISEASE / Precision
      value: 0.82
    - type: recall
      name: Token Scores / DISEASE / Recall
      value: 0.82
    - type: f1
      name: Token Scores / DISEASE / F1
      value: 0.82
    - type: precision
      name: Token Scores / SYMPTOM / Precision
      value: 0.80
    - type: recall
      name: Token Scores / SYMPTOM / Recall
      value: 0.81
    - type: f1
      name: Token Scores / SYMPTOM / F1
      value: 0.80
---
# 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](https://github.com/aphp/edsnlp)**, 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.

| Label        | Description                                                                             |
| ------------ | --------------------------------------------------------------------------------------- |
| `MEDICATION` | Names of drugs or chemical substances used in treatment, e.g., `Metformina`.            |
| `PROCEDURE`  | Medical or surgical procedures performed on a patient, e.g., `biopsia`, `radiografía`.  |
| `DISEASE`    | Diagnosed diseases or medical conditions, e.g., `diabetes mellitus`, `hipertensión`.    |
| `SYMPTOM`    | Reported signs or symptoms experienced by a patient, e.g., `fiebre`, `dolor de cabeza`. |


## Quickstart

1. Install the latest version of edsnlp

    ```shell
    pip install "edsnlp[ml]" -U
    ```
2. Load the model

   ```python
   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_, str(ent._.date))
   ```

To apply the model on many documents using one or more GPUs, refer to the documentation
of [edsnlp](https://aphp.github.io/edsnlp/latest/tutorials/multiple-texts/).

## Metrics

| Token Scores   | Precision | Recall |   F1 |
| :------------- | --------: | -----: | ---: |
| **MEDICATION** |      93.0 |   94.0 | 93.0 |
| **PROCEDURE**  |      85.0 |   85.0 | 85.0 |
| **DISEASE**    |      82.0 |   82.0 | 82.0 |
| **SYMPTOM**    |      80.0 |   81.0 | 80.0 |


## Installation to reproduce

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

```shell
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](https://huggingface.co/PlanTL-GOB-ES/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.