Automating risk of bias assessment for clinical trials.

Systematic reviews, which summarize the entirety of the evidence pertaining to a specific clinical question, have become critical for evidence-based decision making in healthcare. But such reviews have become increasingly onerous to produce due to the exponentially expanding biomedical literature base. This study proposes a step toward mitigating this problem by automating risk of bias assessment in systematic reviews, in which reviewers determine whether study results may be affected by biases (e.g., poor randomization or blinding). Conducting risk of bias assessment is an important but onerous task. We thus describe a machine learning approach to automate this assessment, using the standard Cochrane Risk of Bias Tool which assesses seven common types of bias. Training such a system would typically require a large labeled corpus, which would be prohibitively expensive to collect here. Instead, we use distant supervision, using data from the Cochrane Database of Systematic Reviews (a large repository of systematic reviews), to pseudoannotate a corpus of 2200 clinical trial reports in PDF format. We then develop a joint model which, using the full text of a clinical trial report as input, predicts the risks of bias while simultaneously extracting the text fragments supporting these assessments. This study represents a step toward automating or semiautomating extraction of data necessary for the synthesis of clinical trials.