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        <title>PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks
%A Jin, Di
%A Szolovits, Peter
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the BioNLP 2018 workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F jin-szolovits-2018-pico
%X 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.
%R 10.18653/v1/W18-2308
%U https://aclanthology.org/W18-2308/
%U https://doi.org/10.18653/v1/W18-2308
%P 67-75

Markdown (Informal)

[PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks](https://aclanthology.org/W18-2308/) (Jin & Szolovits, BioNLP 2018)

• PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks (Jin & Szolovits, BioNLP 2018)

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.