Keywords:PICO, Biomedical text, Information extraction
The PICO (Population, Intervention, Comparison, and Outcome) framework is generally used to design clinical trials and researches. Since PICO information is not explicitly labeled in typical medical article texts, automatic identification of PICO information can improve efficiency of medical literature reviews. Although various PICO extraction models, including those based on BERT, have been reported so far, PICO information is sometimes overlapped with each other and none of the previously reported models could extract overlapped information simultaneously. Here we propose an alternative approach in which a layered LSTM model for nested named entity recognition is used in combination with BERT models pre-trained with medical domain texts. We show that the proposed model has comparable performance with the previously reported models, and can simultaneously extract overlapped PICO entities.
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