A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
| Autor: | Byron C. Wallace, Roma Patel, Yinfei Yang, Benjamin E. Nye, Junyi Jessy Li, Iain J. Marshall, Ani Nenkova |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Vocabulary Computer science media_common.quotation_subject Psychological intervention MEDLINE computer.software_genre 01 natural sciences Article law.invention Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Randomized controlled trial law 030212 general & internal medicine 0101 mathematics media_common Data collection Computer Science - Computation and Language business.industry 010102 general mathematics 3. Good health Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing Medical literature |
| Zdroj: | King's College London ACL (1) |
| Popis: | 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. ACL 2018 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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