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A Sentence Classification-Based Medical Status Extraction Pipeline for Electronic Health Records: Institutional Case

Chuanming Dong1, Boris Delange1, Alex Poiron1

  • 1Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France, Rennes, France, +33 02 23 23 62 20.

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Summary
This summary is machine-generated.

The Medical Status Extraction Pipeline (MSEP) efficiently extracts patient medical status from clinical notes, supporting local deployment. It offers a practical solution for institutions needing adaptable clinical information extraction tools.

Keywords:
artificial intelligenceclinical data warehousedeep learninginformation extractionlarge language modelnatural language processingsentence classification

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Area of Science:

  • Clinical Informatics
  • Natural Language Processing (NLP) in Healthcare
  • Medical Data Mining

Background:

  • Clinical data warehouses contain unstructured text with valuable patient medical status information.
  • Traditional Named Entity Recognition (NER) systems struggle to capture contextual cues for accurate interpretation.
  • Existing context-aware extraction methods face limitations in feasibility, scalability, and reusability due to annotation needs, computational power, and data restrictions.

Purpose of the Study:

  • To introduce the Medical Status Extraction Pipeline (MSEP), a framework for extracting patient medical status from clinical narratives.
  • To demonstrate MSEP's capability for local deployment of hybrid extractors through an institutional case study.
  • To provide a practical, adaptable framework for medical institutions to overcome limitations in clinical information extraction.

Main Methods:

  • MSEP classifies sentences into presence, absence, or unknown categories for targeted medical conditions.
  • The pipeline integrates modules for data selection, expert annotation, and customizable model development.
  • Three extractor types (fine-tuned CamemBERT, LLM prompt, rule-based baseline) were evaluated on 12,119 annotated sentences across 6 conditions using cross-validation.

Main Results:

  • The CamemBERT-based extractor achieved high performance (macro F-scores > 0.94 for 5/6 conditions).
  • Rule-based extractors outperformed learned models for sparsely represented data (e.g., family history of cancer).
  • MSEP significantly reduced manual annotation time (1.2-2.9s/sentence) and enabled rapid pipeline completion (min 8 hours).

Conclusions:

  • MSEP facilitates rapid dataset and extractor construction for multiple clinical conditions, reducing local development effort.
  • Its modular and configurable design supports hybrid extraction approaches and adaptation to diverse institutional settings.
  • MSEP serves as a valuable research tool and upstream component for deploying clinical information extraction workflows locally.