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Automatically Linking Registered Clinical Trials to their Published Results with Deep Highway Networks

As medical science continues to advance, health care professionals and researchers are increasingly turning to clinical trials to obtain evidence supporting best-practice treatment options. While clinical trial registries such as Clinical-Trials.gov aim to facilitate these needs, it has been shown t...

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Detalles Bibliográficos
Autores principales: Goodwin, Travis R., Skinner, Michael A., Harabagiu, Sanda M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5961767/
https://www.ncbi.nlm.nih.gov/pubmed/29888040
Descripción
Sumario:As medical science continues to advance, health care professionals and researchers are increasingly turning to clinical trials to obtain evidence supporting best-practice treatment options. While clinical trial registries such as Clinical-Trials.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.