Pretraining to Recognize PICO Elements from Randomized Controlled Trial Literature

PICO (Population/problem, Intervention, Comparison, and Outcome) is widely adopted for formulating clinical questions to retrieve evidence from the literature. It plays a crucial role in Evidence-Based Medicine (EBM). This paper contributes a scalable deep learning method to extract PICO statements...

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Bibliographic Details
Published inStudies in health technology and informatics Vol. 264; p. 188
Main Authors Kang, Tian, Zou, Shirui, Weng, Chunhua
Format Journal Article
LanguageEnglish
Published Netherlands 21.08.2019
Subjects
Evidence-Based Medicine
Randomized Controlled Trial
Natural Language Processing
Online AccessGet more information
ISSN1879-8365
DOI10.3233/SHTI190209

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Summary:PICO (Population/problem, Intervention, Comparison, and Outcome) is widely adopted for formulating clinical questions to retrieve evidence from the literature. It plays a crucial role in Evidence-Based Medicine (EBM). This paper contributes a scalable deep learning method to extract PICO statements from RCT articles. It was trained on a small set of richly annotated PubMed abstracts using an LSTM-CRF model. By initializing our model with pretrained parameters from a large related corpus, we improved the model performance significantly with a minimal feature set. Our method has advantages in minimizing the need for laborious feature handcrafting and in avoiding the need for large shared annotated data by reusing related corpora in pretraining with a deep neural network.
ISSN:1879-8365
DOI:10.3233/SHTI190209