@InProceedings{apostolova-velez:2017:BioNLP17,
  author    = {Apostolova, Emilia  and  Velez, Tom},
  title     = {Toward Automated Early Sepsis Alerting: Identifying Infection Patients from Nursing Notes},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {257--262},
  abstract  = {Severe sepsis and septic shock are conditions that affect millions of patients
	and have close to 50% mortality rate. Early identification of at-risk patients
	significantly improves outcomes. Electronic surveillance tools have been
	developed to monitor structured Electronic Medical Records and automatically
	recognize early signs of sepsis. However, many sepsis risk factors (e.g.
	symptoms and signs of infection) are often captured only in free text clinical
	notes. In this study, we developed a method for automatic monitoring of nursing
	notes for signs and symptoms of infection. We utilized a creative approach to
	automatically generate an annotated dataset. The dataset was used to create a
	Machine Learning model that achieved an F1-score ranging from 79 to 96%.},
  url       = {http://www.aclweb.org/anthology/W17-2332}
}