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Generalizing through Forgetting - Domain Generalization for Symptom Event Extraction in Clinical Notes.

Sitong Zhou1, Kevin Lybarger2, Meliha Yetisgen1

  • 1University of Washington, Seattle, WA, USA.

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Summary

This study introduces a domain generalization method for extracting symptoms from clinical notes, improving information accessibility. The approach enhances symptom extraction accuracy across diverse healthcare settings by using masked language models and adaptive pretraining.

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Biomedical Data Science

Background:

  • Clinical symptom information is often unstructured in free-text notes, limiting its use in downstream applications.
  • Existing information extraction methods struggle with the variability of clinical language across different institutions and specialties.

Purpose of the Study:

  • To develop a domain generalization method for accurate symptom extraction from clinical notes.
  • To improve the adaptability of symptom extraction models to new and unseen clinical domains.

Main Methods:

  • Utilized a transformer-based joint entity and relation extraction model.
  • Implemented a domain generalization technique involving dynamic masking of frequent symptom words in the source domain.
  • Employed adaptive pretraining of the transformer language model on task-related unlabeled texts.

Main Results:

  • The proposed masking and adaptive pretraining methods significantly improved symptom extraction performance.
  • Performance gains were most notable when the source and target domains were substantially different.
  • The approach demonstrated enhanced robustness in handling clinical language variations.

Conclusions:

  • Domain generalization techniques are effective for improving symptom extraction from diverse clinical notes.
  • Adaptive pretraining and dynamic masking are key strategies for building more generalizable clinical NLP models.
  • This work facilitates better access to and utilization of symptom data in healthcare.