Abstract

BackgroundA major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic.ObjectiveTo investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature.MethodsWe trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed’s Clinical Query Broad treatment filter, McMaster’s textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard.ResultsThe deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster’s textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster’s textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001).ConclusionsDeep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis.

Highlights

  • Background and SignificanceWith roughly 95 clinical trials published per day, the biomedical literature is increasing at a very rapid pace, imposing a significant challenge to the practice of evidence-based medicine

  • Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed

  • Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research

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Summary

Introduction

With roughly 95 clinical trials published per day, the biomedical literature is increasing at a very rapid pace, imposing a significant challenge to the practice of evidence-based medicine. A major barrier to the practice of evidence-based medicine is efficiently finding the relatively small number of scientifically sound studies on a given clinical topic. Systematic reviews and meta-analyses attempt to summarize the available evidence on a given clinical question aiming for near perfect recall. Clinicians may benefit from access to the latest evidence from high-quality clinical trials before they are included in systematic reviews. A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic

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