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Peer-Reviewed Publication
Stud Health Technol Inform2019;2641512-1513.August 21, 2019Journal Article

AutoScribe: Extracting Clinically Pertinent Information from Patient-Clinician Dialogues.

Faiza Khan Khattak1,2, Serena Jeblee1,2, Noah Crampton3, Muhammad Mamdani3, Frank Rudzicz1,2,3,4
1Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
2Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.
3Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada.
4Surgical Safety Technologies Inc, Toronto, Ontario, Canada.
PubMed DOI

Abstract

We present AutoScribe, a system for automatically extracting pertinent medical information from dialogues between clinicians and patients. AutoScribe parses the dialogue and extracts entities such as medications and symptoms, using context to predict which entities are relevant, and automatically generates a patient note and primary diagnosis.

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