Pain and anesthesia information are crucial elements to identifying surgery-related processes and outcomes. However pain is not consistently recorded in the electronic medical record. Even when recorded, the rich complex granularity of the pain experience may be lost. Similarly, anesthesia information is recorded using local electronic collection systems; though the accuracy and completeness of the information is unknown. We propose an annotation schema to capture pain, pain management, and anesthesia event information.
CITATION STYLE
Yim, W. W., Tedesco, D., Curtin, C., & Hernandez-Boussard, T. (2017). Annotation of pain and anesthesia events for surgery-related processes and outcomes extraction. In BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop (pp. 200–205). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-2325
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