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

Machine Learning Medical Informatics Medical Records

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
21 Aug 2019
Historique:
entrez: 24 8 2019
pubmed: 24 8 2019
medline: 5 9 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31438207
pii: SHTI190510
doi: 10.3233/SHTI190510
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1512-1513

Auteurs

Faiza Khan Khattak (FK)

Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.

Serena Jeblee (S)

Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.

Noah Crampton (N)

Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada.

Muhammad Mamdani (M)

Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada.

Frank Rudzicz (F)

Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.
Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada.
Surgical Safety Technologies Inc, Toronto, Ontario, Canada.

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Classifications MeSH