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Improving Endpoint Detection to Support Automated Systematic Reviews.

Ana Lucic1, Catherine L Blake1

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AMIA ... Annual Symposium Proceedings. AMIA Symposium
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Summary

Identifying comparison sentence endpoints in biomedical texts is challenging. Distinguishing amounts or measures as endpoints significantly improves automated comparison claim identification accuracy.

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Area of Science:

  • Biomedical informatics
  • Natural language processing
  • Scientific literature analysis

Background:

  • Authors of biomedical articles use comparison sentences to convey study findings and compare results with prior research.
  • The Claim Framework defines comparison claims by two entities and a comparative endpoint.
  • Automated methods struggle to differentiate between entities and endpoints in comparison sentences, particularly the first entity.

Purpose of the Study:

  • To improve automated identification of comparison claims in biomedical literature.
  • To enhance the differentiation between entities and endpoints within comparison sentences.
  • To evaluate the impact of identifying head nouns as amounts or measures on endpoint classification accuracy.

Main Methods:

  • Developed and tested a classification model to identify comparison sentences and their components (entities, endpoints).
  • Empirically evaluated the contribution of recognizing head nouns as 'amount' or 'measure' to endpoint identification.
  • Assessed performance on both longer and shorter sentences, analyzing precision and recall.

Main Results:

  • Identifying head nouns as amounts or measures significantly improved endpoint precision.
  • Precision increased from 0.42 to 0.56 (longer sentences) and 0.51 to 0.58 (shorter sentences).
  • Recall improved from 0.64 to 0.71 (longer sentences) and 0.69 to 0.74 (shorter sentences).

Conclusions:

  • Recognizing head nouns as amounts or measures is a statistically significant improvement for identifying comparison sentence endpoints.
  • This approach enhances the accuracy of automated comparison claim extraction in biomedical texts.
  • Further improvements for differentiating the second compared entity were not statistically significant.