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Updated: Oct 12, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Are Semantic Annotators Able to Extract Relevant Complexity-Related Concepts from Clinical Notes?

Akram Redjdal1, Jacques Bouaud1,2, Joseph Gligorov3,4

  • 1Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMRS_1142, LIMICS, Paris, France.

Studies in Health Technology and Informatics
|November 19, 2021
PubMed
Summary

Predicting complex cancer cases before multidisciplinary tumor boards (MTBs) is crucial. Semantic annotators can extract complexity reasons from breast cancer patient summaries (BCPSs), improving guideline adherence.

Keywords:
Breast CancerDecision SupportInformation Extraction

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Area of Science:

  • Oncology
  • Medical Informatics
  • Natural Language Processing

Background:

  • Clinical decision support systems (CDSSs) aim to improve adherence to cancer clinical practice guidelines (CPGs).
  • Current guideline-based CDSSs often fail to address complex cancer cases, necessitating further discussion within multidisciplinary tumor boards (MTBs).

Purpose of the Study:

  • To develop a method for predicting complex cancer cases prior to MTBs using breast cancer patient summaries (BCPSs).
  • To evaluate the effectiveness of semantic annotators in extracting complexity-related concepts from unstructured clinical notes.

Main Methods:

  • Implementation of four semantic annotators (ECMT, SIFR, cTAKES, MetaMap) to process unstructured BCPSs.
  • Assessment of the annotators' performance in identifying complexity reasons within a sample of 24 BCPSs.

Main Results:

  • ECMT and MetaMap demonstrated the highest individual performance rates at 60% and 49%, respectively, in extracting complexity reasons.
  • Utilizing all four annotators sequentially enabled the extraction of 69% of complexity reasons from the BCPSs.

Conclusions:

  • Semantic annotation of clinical notes holds promise for identifying complex cancer cases.
  • A sequential approach using multiple annotators can enhance the extraction of complexity-related information, potentially aiding MTB preparation and decision-making.