@inproceedings{L16-1545,
 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. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7\% and 91.8\%, respectively.
},
 address = {Portorož, Slovenia},
 author = {Prescott Klassen and Fei Xia and Meliha Yetisgen},
 booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
 month = {May},
 pages = {3417--3421},
 publisher = {European Language Resources Association (ELRA)},
 title = {Annotating and Detecting Medical Events in Clinical Notes},
 url = {https://www.aclweb.org/anthology/L16-1545},
 year = {2016}
}