Paper Info
Title
PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks.
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.
Year
Venue
DocType
2018
SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2018)
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Di Jin124.09
Peter Szolovits2852222.19