Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation

Linh Hoang, Richard D. Boyce, Nigel Bosch, Britney Stottlemyer, Mathias Brochhausen, Jodi Schneider

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized support might help experts be more objective in assessing DDI evidence. A requirement for such systems is accurate automatic classification of evidence types. In this pilot study, we developed a hierarchical classifier to classify clinical DDI studies into formally defined evidence types. The area under the ROC curve for sub-classifiers in the ensemble ranged from 0.78 to 0.87. The entire system achieved an F1 of 0.83 and 0.63 on two held-out datasets, the latter consisting focused on completely novel drugs from what the system was trained on. The results suggest that it is feasible to accurately automate the classification of a sub-set of DDI evidence types and that the hierarchical approach shows promise. Future work will test more advanced feature engineering techniques while expanding the system to classify a more complex set of evidence types.

Original languageEnglish (US)
Title of host publication2020 AMIA Annual Symposium Proceedings
Pages554-563
Number of pages10
StatePublished - Nov 2020

ASJC Scopus subject areas

  • Medicine(all)

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