This dataset is introduced by Jin, Di, and Peter Szolovits. "PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks." Proceedings of the BioNLP 2018 workshop. 2018..
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.
Some miscellaneous information:
structured_abstracts_PICOcontains the original abstracts. The line that starts with###indicates the PMID. After that line, each line contains the original section heading, the assgined gold label for train and test and the section content, separated by the symbol|. To create the gold label, key words in the section heading are checked and the mapping rule can be referred to the paper above-mentioned.structured_abstracts_sentences_PICOis almost the same asstructured_abstracts_PICOexcept that each section conent is sentence splitted using the Stanford CoreNLP toolkit so that each line has only one sentence and all numbers have been replaced by@.- The folder
splittedcontains the train, validation and test sets that are randomly splitted from the filestructured_abstracts_sentences_PICOat the ratio of 8:1:1.
You are most welcome to share with us your analyses or work using this dataset by citing our paper!