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Towards generalizable entity-centric clinical coreference resolution.

Timothy Miller1, Dmitriy Dligach2, Steven Bethard3

  • 1Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.

Journal of Biomedical Informatics
|April 26, 2017
PubMed
Summary
This summary is machine-generated.

A new mention-synchronous system for clinical coreference resolution shows improved generalizability across different datasets. While performing similarly on in-domain data, it significantly outperforms baseline methods on new data, indicating better model adaptability.

Keywords:
Clinical NLPCoreferenceGeneralizabilityMachine learningPortability

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics

Background:

  • Clinical coreference resolution is crucial for understanding patient records.
  • Evaluating the generalizability of clinical NLP models is essential but under-studied.

Purpose of the Study:

  • To compare the performance of a baseline mention-pair coreference system with a mention-synchronous system.
  • To measure model performance in both in-domain and cross-domain settings.
  • To assess the generalizability of learned clinical NLP models.

Main Methods:

  • Developed a mention-synchronous coreference system and a baseline mention-pair system.
  • Incorporated distributional semantics, discourse features, and entity attributes.
  • Utilized the THYME colon cancer and DeepPhe breast cancer datasets for evaluation.

Main Results:

  • The mention-synchronous system achieved similar performance on in-domain data compared to the baseline.
  • The mention-synchronous system demonstrated significantly better performance on new (cross-domain) data.
  • Part-of-speech tag features showed superior generalizability compared to other word representations.

Conclusions:

  • The mention-synchronous system offers improved generalizability for clinical coreference resolution.
  • Further research should focus on cross-domain and cross-institution evaluations to enhance model adaptability.
  • An optimized version of the mention-synchronous system will be integrated into Apache cTAKES.