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
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 67–75
- Language:
- URL:
- https://aclanthology.org/W18-2308
- DOI:
- 10.18653/v1/W18-2308
- 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, BioNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-2308.pdf
- Code
- jind11/PubMed-PICO-Detection + additional community code
- Data
- PubMed PICO Element Detection Dataset