Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning | Zendy

John D. Osborne | Zendy; Matthew Wyatt | Zendy; Andrew O. Westfall | Zendy; James H. Willig | Zendy; Steven Bethard | Zendy; Geoff Gordon | Zendy

Open Access

Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning

Author(s) -

John D. Osborne,

Matthew Wyatt,

Andrew O. Westfall,

James H. Willig,

Steven Bethard,

Geoff Gordon

Publication year - 2016

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocw006

Subject(s) - computer science , recall , cancer , identification (biology) , precision and recall , artificial intelligence , precision medicine , filter (signal processing) , pipeline (software) , interface (matter) , machine learning , medicine , pathology , programming language , philosophy , linguistics , botany , computer vision , biology , bubble , maximum bubble pressure method , parallel computing

To help cancer registrars efficiently and accurately identify reportable cancer cases.

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