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Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types.

Nuttapong Sanglerdsinlapachai1, Anon Plangprasopchok2, Tu Bao Ho3

  • 1National Electronics and Computer Technology Center, Pathumthani, Thailand; Japan Advanced Institute of Science and Technology, Ishikawa, Japan; Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand.

Artificial Intelligence in Medicine
|March 9, 2021
PubMed
Summary

This study enhances clinical sentiment analysis by using Unified Medical Language System (UMLS) semantic types. Leveraging these types significantly improves the accuracy of identifying patient health status from clinical narratives.

Keywords:
Classifier combinationClinical narrativeDomain-specific knowledgeLexicon-based sentiment analysisUnified medical language system

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Computational Linguistics

Background:

  • Clinical narratives contain sentiment crucial for patient health status assessment.
  • Accurate sentiment analysis of clinical text requires domain-specific medical knowledge.
  • Existing lexicon-based methods often lack the necessary medical context.

Purpose of the Study:

  • To improve sentiment classification accuracy in clinical narratives.
  • To explore the utility of Unified Medical Language System (UMLS) semantic types for sentiment analysis.
  • To develop and evaluate advanced methods for clinical sentiment analysis.

Main Methods:

  • Utilized semantic types from the Unified Medical Language System (UMLS) to enhance lexicon-based sentiment classification.
  • Applied logistic regression to determine polarity scores for UMLS 'Disorders' semantic types with SentiWordNet.
  • Replaced medical terms with their corresponding UMLS semantic types in trained lexicons.
  • Proposed and implemented a classifier combination strategy for improved performance on diverse data segments.

Main Results:

  • Sentiment classification accuracy improved from 0.582 to 0.710 using logistic regression with UMLS 'Disorders' semantic types.
  • Replacing specific disorder terms with semantic types enhanced classification accuracy on certain data segments.
  • Classifier combination led to improved accuracies across most data segments, achieving an overall accuracy of 0.882.

Conclusions:

  • Integrating UMLS semantic types significantly enhances the accuracy of clinical sentiment analysis.
  • The proposed classifier combination method offers a robust approach for diverse clinical text data.
  • This research provides a valuable framework for improving automated patient health status assessment through NLP.