cleanote 0.3.5

CleanNote: preprocessing and harmonization of medical notes

CleanNote

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English

CleanNote analyzes raw medical notes and transforms them into concise, structured documents focused on symptoms, medical conclusions, and treatments, enabling easier analysis and clinical research.

This solution was developed during a research internship at the Hubert Curien laboratory, and is currently demonstrated using the AGBonnet/Augmented-clinical-notes dataset together with the mistralai/Mistral-7B-Instruct-v0.3 model.

This work was supervised by three people:

• Ms. Combes Catherine, company supervisor and associate professor, who proposed the topic of the preprocessing pipeline,

• Ms. Fresse Virginie, school supervisor and associate professor, who guided me on frugality aspects and formatting,

• Mr. ****, PhD Hatoum Carl, who provided support on identifying adapted solutions and domain knowledge.

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Installation

First, make sure you are inside your Python virtual environment (e.g. venv).
To install the latest available version (see the PyPI badge above):

 pip install -U cleanote

If you want to install a specific version (for example 0.2.1):

 pip install -U cleanote==0.2.1

The latest released version is always displayed in the PyPI badge at the top of this README.

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Usage

After installation, you can using CleanNote with just few lines of code:

from cleanote.pipeline import Pipeline
from cleanote.dataset import Dataset
from cleanote.model import Model

data = Dataset(name="AGBonnet/Augmented-clinical-notes", split="train", field="full_note", limit=20)

prompt = """Analyze the document below and return a single, valid JSON object with exactly these keys (no trailing commas):
        {{
            "Symptoms": [/* list of symptoms extracted from the document */],
            "MedicalConclusion": [/* list of medical conclusion extracted from the document */],
            "Treatments": [/* list of treatments extracted from the document */],
            "Summary": "/* a professional paragraph summarizing the note, that mentions only items already listed in Symptoms, MedicalConclusion, and Treatments, without inventing anything */"
        }}

            - If no information exists for a given key, return "none" for that key.
            - The Summary field **must use only** the items already extracted above and **must not add** any new facts.
            - Ensure the output is **syntactically valid JSON**.

            Document:
            """

ml_h = Model(
    name="mistralai/Mistral-7B-Instruct-v0.3",
    task="text-generation",
    prompt=prompt,
    max_new_tokens=1024,   
    do_sample=False,
    temperature=0.0,
    top_p=1.0)

pipe = Pipeline(dataset=data, model_h = ml_h)

out = pipe.apply()

pipe.save_all_stats_images(limit=20)

xls = pipe.to_excel()  
print(f"Excel file saved : {xls}")

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Literature

• Identification de profils patients à partir de notes cliniques non structurées.
  Corentin Laval, Catherine Combes, Rémi Eyraud, Virginie Fresse.
  PFIA 2025.

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Français

CleanNote analyse des notes médicales brutes et les transforme en documents concis et structurés, centrés sur les symptômes, les conclusions médicales et les traitements, afin de faciliter leur analyse et la recherche clinique.

Cette solution a été développée dans le cadre d’un stage de recherche au laboratoire Hubert Curien , et est actuellement démontrée à l’aide du jeu de données AGBonnet/Augmented-clinical-notes ainsi que du modèle mistralai/Mistral-7B-Instruct-v0.3.

Ce travail a été supervisé par trois personnes :

• Mme Combes Catherine, tutrice entreprise et maîtresse de conférences, qui a proposé le sujet du pipeline de prétraitement,

• Mme Fresse Virginie, tutrice école et maîtresse de conférences, qui m’a encadré sur les aspects de frugalité et de mise en forme,

• M. Hatoum Carl, doctorant, qui m’a accompagné sur l’identification des solutions adaptées et l’apport de connaissances du domaine.

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Installation

Tout d’abord, assurez-vous d’être dans votre environnement virtuel Python (par ex. venv).
Pour installer la dernière version disponible (voir le badge PyPI ci-dessus) :

 pip install -U cleanote

Si vous souhaitez installer une version spécifique (par exemple 0.2.1):

 pip install -U cleanote==0.2.1

La dernière version publiée est toujours affichée dans le badge PyPI en haut de ce README.

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Utilisation

Après installation, vous pouvez utiliser CleanNote en seulement quelques lignes de code :

from cleanote.pipeline import Pipeline
from cleanote.dataset import Dataset
from cleanote.model import Model

data = Dataset(name="AGBonnet/Augmented-clinical-notes", split="train", field="full_note", limit=20)

prompt = """Analyze the document below and return a single, valid JSON object with exactly these keys (no trailing commas):
        {{
            "Symptoms": [/* list of symptoms extracted from the document */],
            "MedicalConclusion": [/* list of medical conclusion extracted from the document */],
            "Treatments": [/* list of treatments extracted from the document */],
            "Summary": "/* a professional paragraph summarizing the note, that mentions only items already listed in Symptoms, MedicalConclusion, and Treatments, without inventing anything */"
        }}

            - If no information exists for a given key, return "none" for that key.
            - The Summary field **must use only** the items already extracted above and **must not add** any new facts.
            - Ensure the output is **syntactically valid JSON**.

            Document:
            """

ml_h = Model(
    name="mistralai/Mistral-7B-Instruct-v0.3",
    task="text-generation",
    prompt=prompt,
    max_new_tokens=1024,   
    do_sample=False,
    temperature=0.0,
    top_p=1.0)

pipe = Pipeline(dataset=data, model_h = ml_h)

out = pipe.apply()

pipe.save_all_stats_images(limit=20)

xls = pipe.to_excel()  
print(f"Excel file saved : {xls}")

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Références

• Identification de profils patients à partir de notes cliniques non structurées.
  Corentin Laval, Catherine Combes, Rémi Eyraud, Virginie Fresse.
  PFIA 2025.

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License

This project is licensed under the MIT License.