首页|Automatic endpoint detection to support the systematic review process
Automatic endpoint detection to support the systematic review process
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NSTL
Elsevier
Preparing a systematic review can take hundreds of hours to complete, but the process of reconciling different results from multiple studies is the bedrock of evidence-based medicine. We introduce a two-step approach to automatically extract three facets - two entities (the agent and object) and the way in which the entities are compared (the endpoint) - from direct comparative sentences in full-text articles. The system does not require a user to predefine entities in advance and thus can be used in domains where entity recognition is difficult or unavailable. As with a systematic review, the tabular summary produced using the automatically extracted facets shows how experimental results differ between studies. Experiments were conducted using a collection of more than 2 million sentences from three journals Diabetes, Carcinogenesis and Endocrinology and two machine learning algorithms, support vector machines (SVM) and a general linear model (GLM). F-l and accuracy measures for the SVM and GLM differed by only 0.01 across all three comparison facets in a randomly selected set of test sentences. The system achieved the best performance of 92% for objects, whereas the accuracy for both agent and endpoints was 73%. F-1 scores were higher for objects (0.77) than for endpoints (0.51) or agents (0.47). A situated evaluation of Metformin, a drug to treat diabetes, showed system accuracy of 95%, 83% and 79% for the object, endpoint and agent respectively. The situated evaluation had higher F-1 scores of 0.88, 0.64 and 0.62 for object, endpoint, and agent respectively. On average, only 5.31% of the sentences in a full-text article are direct comparisons, but the tabular summaries suggest that these sentences provide a rich source of currently underutilized information that can be used to accelerate the systematic review process and identify gaps where future research should be focused. (C) 2015 Elsevier Inc. All rights reserved.
NSTL
Elsevier
