PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks

Di Jin, Peter Szolovits


Abstract
Successful evidence-based medicine (EBM) applications rely on answering clinical questions by analyzing large medical literature databases. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical text that belong to the four components: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). In this work, we present a Long Short-Term Memory (LSTM) neural network based model to automatically detect PICO elements. By jointly classifying subsequent sentences in the given text, we achieve state-of-the-art results on PICO element classification compared to several strong baseline models. We also make our curated data public as a benchmarking dataset so that the community can benefit from it.
Anthology ID:
W18-2308
Volume:
Proceedings of the BioNLP 2018 workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | BioNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
67–75
Language:
URL:
https://aclanthology.org/W18-2308
DOI:
10.18653/v1/W18-2308
Bibkey:
Cite (ACL):
Di Jin and Peter Szolovits. 2018. PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks. In Proceedings of the BioNLP 2018 workshop, pages 67–75, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks (Jin & Szolovits, 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2308.pdf
Code
 jind11/PubMed-PICO-Detection +  additional community code
Data
PubMed PICO Element Detection Dataset