Summary of the paper

Title Annotating Clinical Events in Text Snippets for Phenotype Detection
Authors Prescott Klassen, Fei Xia, Lucy Vanderwende and Meliha Yetisgen
Abstract Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. NLP systems that analyze the narrative data embedded in clinical artifacts such as x-ray reports can help support early detection. In this paper, we consider the importance of identifying the change of state for events - in particular, clinical events that measure and compare the multiple states of a patient’s health across time. We propose a schema for event annotation comprised of five fields and create preliminary annotation guidelines for annotators to apply the schema. We then train annotators, measure their performance, and finalize our guidelines. With the complete guidelines, we then annotate a corpus of snippets extracted from chest x-ray reports in order to integrate the annotations as a new source of features for classification tasks.
Topics Document Classification, Text categorisation, Knowledge Discovery/Representation
Full paper Annotating Clinical Events in Text Snippets for Phenotype Detection
Bibtex @InProceedings{KLASSEN14.386,
  author = {Prescott Klassen and Fei Xia and Lucy Vanderwende and Meliha Yetisgen},
  title = {Annotating Clinical Events in Text Snippets for Phenotype Detection},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  month = {may},
  date = {26-31},
  address = {Reykjavik, Iceland},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-8-4},
  language = {english}
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