Deploying an Interactive Machine Learning System in an Evidence-based Practice Center: Abstrackr

Deploying an Interactive Machine Learning System in an Evidence-based Practice Center: Abstrackr

Wallace BC; Small K; Brodley CE; Lau J; Trikalinos TA

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

2012

Evidence-Based Medicine; Medical; active learning; applications; machine learning; Scoping Review 2015-2016; text classification

Medical researchers looking for evidence pertinent to a specific clinical question must navigate an increasingly voluminous corpus of published literature. This data deluge has motivated the development of machine learning and data mining technologies to facilitate efficient biomedical research. Despite the obvious labor-saving potential of these technologies and the concomitant academic interest therein, however, adoption of machine learning techniques by medical researchers has been relatively sluggish. One explanation for this is that while many machine learning methods have been proposed and retrospectively evaluated, they are rarely (if ever) actually made accessible to the practitioners whom they would benefit. In this work, we describe the ongoing development of an end-to-end interactive machine learning system at the Tufts Evidence-based Practice Center. More specifically, we have developed abstrackr, an online tool for the task of citation screening for systematic reviews. This tool provides an interface to our machine learning methods. The main aim of this work is to provide a case study in deploying cutting-edge machine learning methods that will actually be used by experts in a clinical research setting.

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