Advancing PICO Element Detection in Medical Text via Deep Neural Networks
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In evidence-based medicine (EBM), structured medical questions are always favored for efficient search of the best available evidence for treatments. PICO element detection is widely used to help structurize the clinical studies and question by identifying the sentences in a given medical text that belong to one of the four components: Participants (P), Intervention (I), Comparison (C), and Outcome (O). In this work, we propose a hierarchical deep neural network (DNN) architecture that contains dual bi-directional long short-term memory (bi-LSTM) layers to automatically detect the PICO element in medical texts. Within the model, the lower layer of bi-LSTM is for sentence encoding while the upper one is to contextualize the encoded sentence representation vector. In addition, we adopt adversarial and virtual adversarial training to regularize the model. Overall, we advance the PICO element detection to new state-of-the-art performance, outperforming the previous works by at least 4% in F1 score for all P/I/O categories.
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