Classifying Biomedical Abstracts Using Committees of Classifiers and Collective Ranking Techniques

Authors:
Alexandre Kouznetsov;Stan Matwin;Diana Inkpen;Amir H. Razavi;Oana Frunza;Morvarid Sehatkar;Leanne Seaward;Peter O'Blenis
Affiliations:
School of Information Technology and Engineering (SITE), University of Ottawa,;School of Information Technology and Engineering (SITE), University of Ottawa,;School of Information Technology and Engineering (SITE), University of Ottawa,;School of Information Technology and Engineering (SITE), University of Ottawa,;School of Information Technology and Engineering (SITE), University of Ottawa,;School of Information Technology and Engineering (SITE), University of Ottawa,;School of Information Technology and Engineering (SITE), University of Ottawa,;TrialStat Corporation, Ottawa, CA K2C 3W7
Venue:
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2009

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Abstract

The purpose of this work is to reduce the workload of human experts in building systematic reviews from published articles, used in evidence-based medicine. We propose to use a committee of classifiers to rank biomedical abstracts based on the predicted relevance to the topic under review. In our approach, we identify two subsets of abstracts: one that represents the top, and another that represents the bottom of the ranked list. These subsets, identified using machine learning (ML) techniques, are considered zones where abstracts are labeled with high confidence as relevant or irrelevant to the topic of the review. Early experiments with this approach using different classifiers and different representation techniques show significant workload reduction.