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Updated: May 17, 2025

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Weakly supervised text classification on free-text comments in patient-reported outcome measures.

Anna-Grace Linton1, Vania Gatseva Dimitrova2, Amy Downing3

  • 1UKRI CDT in AI for Medical Diagnosis and Care, University of Leeds, Leeds, United Kingdom.

Frontiers in Digital Health
|May 15, 2025
PubMed
Summary

Weakly supervised text classification (WSTC) effectively analyzes patient-reported outcome measure (PROM) comments to identify health-related quality of life (HRQoL) themes. Keyword-based WSTC methods show potential for limited labeled data, with some achieving high accuracy on specific themes.

Keywords:
PROMSfree-textnatural language processingpatient-generated datapatient-reported datashort texttext classificationweakly supervised

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Area of Science:

  • Computational Linguistics
  • Health Informatics
  • Machine Learning

Background:

  • Patient-reported outcome measures (PROMs) capture valuable health-related quality of life (HRQoL) data through free-text comments.
  • Manual analysis of these comments is labor-intensive and time-consuming.
  • Existing machine learning methods often require extensive labeled data and post-analysis interpretation.

Purpose of the Study:

  • To evaluate the effectiveness of five weakly supervised text classification (WSTC) techniques for analyzing PROMs comments.
  • To identify health-related quality of life (HRQoL) themes in cancer patient data using WSTC.
  • To assess the performance and interpretability of WSTC methods with limited labeled data.

Main Methods:

  • A scoping review identified key HRQoL themes and keywords.
  • Five keyword-based WSTC methods (anchored CorEx, BERTopic, Guided LDA, WeSTClass, X-Class) were applied to colorectal and prostate cancer PROMs datasets.
  • Performance was evaluated by overall and theme-specific metrics, with domain expert review for interpretability.

Main Results:

  • Six main HRQoL themes were identified: Comorbidities, Daily Life, Health Pathways and Services, Physical Function, Psychological and Emotional Function, and Social Function.
  • Method performance varied, with anchored CorEx achieving weighted F1 scores of 0.57 (colorectal) and 0.61 (prostate).
  • Individual themes reached F1 scores up to 0.92, and methods utilizing expert seed terms and extrapolating from limited data performed best.

Conclusions:

  • Keyword-based WSTC methods show significant potential for analyzing PROMs comments, particularly when labeled data is scarce.
  • The study highlights both the capabilities and limitations of WSTC in accurately classifying HRQoL themes from patient-generated text.
  • WSTC offers a promising avenue for efficient and scalable analysis of qualitative patient feedback in healthcare research.