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info.json
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{
"abstract": " <em>Systematic reviews</em> underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a <em>PICO</em> criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive <em>distant supervision</em> (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method -- <em>supervised distant supervision</em> (SDS) -- that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by <em>learning</em> to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.",
"authors": [
"Byron C. Wallace",
"Jo{{\\\"e}}l Kuiper",
"Aakash Sharma",
"Mingxi (Brian) Zhu",
"Iain J. Marshall"
],
"id": "15-404",
"issue": 132,
"pages": [
1,
25
],
"title": "Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision",
"volume": 17,
"year": 2016
}