1 research outputs found
Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks
In evidence-based medicine (EBM), defining a clinical question in terms of
the specific patient problem aids the physicians to efficiently identify
appropriate resources and search for the best available evidence for medical
treatment. 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 typically reported in clinical
trials: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome
(O). In this work, we propose a novel deep learning model for recognizing PICO
elements in biomedical abstracts. Based on the previous state-of-the-art
bidirectional long-short term memory (biLSTM) plus conditional random field
(CRF) architecture, we add another layer of biLSTM upon the sentence
representation vectors so that the contextual information from surrounding
sentences can be gathered to help infer the interpretation of the current one.
In addition, we propose two methods to further generalize and improve the
model: adversarial training and unsupervised pre-training over large corpora.
We tested our proposed approach over two benchmark datasets. One is the
PubMed-PICO dataset, where our best results outperform the previous best by
5.5%, 7.9%, and 5.8% for P, I, and O elements in terms of F1 score,
respectively. And for the other dataset named NICTA-PIBOSO, the improvements
for P/I/O elements are 2.4%, 13.6%, and 1.0% in F1 score, respectively.
Overall, our proposed deep learning model can obtain unprecedented PICO element
detection accuracy while avoiding the need for any manual feature selection.Comment: Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended
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