Deploying an interactive machine learning system in an evidence-based practice center

TLDR

The expanding volume of biomedical literature forces researchers to sift through vast corpora, prompting the development of machine‑learning and data‑mining tools that, despite their labor‑saving potential, remain under‑adopted because they are rarely made accessible to practitioners. This study aims to demonstrate the deployment of cutting‑edge machine‑learning methods in a real clinical research setting by providing a case study from the Tufts Evidence‑based Practice Center. We developed abstrackr, an online tool for citation screening in systematic reviews that offers an interactive interface to our machine‑learning algorithms. The tool successfully exposes the underlying machine‑learning methods to users through its interface.

Abstract

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.