Summary of the paper

Title Medical Entity Corpus with PICO elements and Sentiment Analysis
Authors Markus Zlabinger, Linda Andersson, Allan Hanbury, Michael Andersson, Vanessa Quasnik and Jon Brassey
Abstract In this paper, we present our process to establish a PICO and a sentiment annotated corpus of clinical trial publications. PICO stands for Population, Intervention, Comparison and Outcome --- these four classes can be used for more advanced and specific search queries. For example, a physician can determine how well a drug works only in the subgroup of children. Additionally to the PICO extraction, we conducted a sentiment annotation, where the sentiment refers to whether the conclusion of a trial was positive, negative or neutral. We created both corpora with the help of medical experts and non-experts as annotators.
Topics Opinion Mining / Sentiment Analysis, Text Mining, Corpus (Creation, Annotation, Etc.)
Full paper Medical Entity Corpus with PICO elements and Sentiment Analysis
Bibtex @InProceedings{ZLABINGER18.1088,
  author = {Markus Zlabinger and Linda Andersson and Allan Hanbury and Michael Andersson and Vanessa Quasnik and Jon Brassey},
  title = "{Medical Entity Corpus with PICO elements and Sentiment Analysis}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
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