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A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

Overview
Journal Proc Conf Assoc Comput Linguist Meet
Date 2018 Oct 12
PMID 30305770
Citations 52
Authors
Benjamin Nye
Junyi Jessy Li
Roma Patel
Yinfei Yang
Iain J Marshall
Ani Nenkova
Byron C Wallace
Affiliations
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Abstract

We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the 'PICO' elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.

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References
1.
Kiritchenko S, De Bruijn B, Carini S, Martin J, Sim I . ExaCT: automatic extraction of clinical trial characteristics from journal publications. BMC Med Inform Decis Mak. 2010; 10:56. PMC: 2954855. DOI: 10.1186/1472-6947-10-56. View
2.
Chung G . Sentence retrieval for abstracts of randomized controlled trials. BMC Med Inform Decis Mak. 2009; 9:10. PMC: 2657779. DOI: 10.1186/1472-6947-9-10. View
3.
Lu Z, Kim W, Wilbur W . Evaluation of Query Expansion Using MeSH in PubMed. Inf Retr Boston. 2009; 12(1):69-80. PMC: 2747526. DOI: 10.1007/s10791-008-9074-8. View
4.
Wallace B, Noel-Storr A, Marshall I, Cohen A, Smalheiser N, Thomas J . Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach. J Am Med Inform Assoc. 2017; 24(6):1165-1168. PMC: 5975623. DOI: 10.1093/jamia/ocx053. View
5.
Tsafnat G, Dunn A, Glasziou P, Coiera E . The automation of systematic reviews. BMJ. 2013; 346:f139. DOI: 10.1136/bmj.f139. View