Predicting Chief Complaints at Triage Time in the Emergency Department
Yacine Jernite, Yoni Halpern, Steven Horng, David Sontag
As hospitals increasingly use electronic medical records for research and quality improvement, it is important to provide ways to structure medical data without losing either expressiveness or time. We present a system that helps achieve this goal by building an extended ontology of chief complaints and automatically predicting a patient’s chief complaint, based on their vitals and the nurses’ description of their state at arrival.
NeurIPS Workshop on Machine Learning for Clinical Data Analysis and Healthcare
Research Scientist, Hugging Face
Professor of EECS
My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.