@InProceedings{jin-szolovits:2018:BioNLP18,
  author    = {Jin, Di  and  Szolovits, Peter},
  title     = {PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks},
  booktitle = {Proceedings of the BioNLP 2018 workshop},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {67--75},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-2308}
}