@InProceedings{viani-EtAl:2018:LOUHI,
  author    = {Viani, Natalia  and  Yin, Lucia  and  Kam, Joyce  and  Alawi, Ayunni  and  Bittar, André  and  Dutta, Rina  and  Patel, Rashmi  and  Stewart, Robert  and  Velupillai, Sumithra},
  title     = {Time Expressions in Mental Health Records for Symptom Onset Extraction},
  booktitle = {Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {183--192},
  abstract  = {For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development.},
  url       = {http://www.aclweb.org/anthology/W18-5621}
}