Abstract: As medical science continues to advance, health care professionals are increasingly turning to clinical trials to obtain evidence supporting best-practice treatment options. While clinical trial registries such as ClinicalTrials.gov aim to facilitate these needs, it has been shown that many trials in the registry do not contain links to their published results. To address this problem, we present NCT Link, a system for automatically linking registered clinical trials to published MEDLINE articles reporting their results. NCT Link incorporates state-of-the-art deep learning and information retrieval techniques by automatically learning a Deep Highway Network (DHN) that estimates the likelihood that a MEDLINE article reports the results of a clinical trial. Our experimental results indicate that NCT Link obtains 30%-58% improved performance over previously reported automatic systems, suggesting that NCT Link could become a valuable tool for health care providers seeking to deliver best-practice medical care informed by evidence of clinical trials as well as (a) researchers investigating selective publication and reporting of clinical trial outcomes, and (b) study designers seeking to avoid unnecessary duplication of research efforts.

Learning Objective 1: automatically link clinical trials to publications reporting their results

Learning Objective 2 (Optional): design and implement a Deep Highway Network

Learning Objective 3 (Optional): extract features characterizing the relationship between a registered clinical trial a published article

Learning Objective 4 (Optional): design a custom, offline index of MEDLINE


Travis Goodwin (Presenter)
University of Texas at Dallas

Michael Skinner, University of Texas at Dallas
Sanda Harabagiu, University of Texas at Dallas

Presentation Materials: