A machine learning approach for identifying anatomical locations of actionable findings in radiology reports.

AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

Volumn 2012, Issue , 2012, Pages 779-788

  Roberts, Kirk ^a   Rink, Bryan ^b   Harabagiu, Sanda M ^b   Scheuermann, Richard H ^b   Toomay, Seth ^b   Browning, Travis ^b   Bosler, Teresa ^b   Peshock, Ronald ^b  

^a UNIVERSITY OF TEXAS AT DALLAS   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

ANATOMY; APPENDICITIS; ARTICLE; ARTIFICIAL INTELLIGENCE; HOSPITAL INFORMATION SYSTEM; HUMAN; METHODOLOGY; NATURAL LANGUAGE PROCESSING; PATHOLOGY; RADIOGRAPHY; UNIFIED MEDICAL LANGUAGE SYSTEM;

ANATOMY; APPENDICITIS; ARTIFICIAL INTELLIGENCE; HUMANS; NATURAL LANGUAGE PROCESSING; RADIOLOGY INFORMATION SYSTEMS; UNIFIED MEDICAL LANGUAGE SYSTEM;

EID: 84880840399     PISSN: None     EISSN: 1942597X     Source Type: None    
DOI: None     Document Type: Article

References (0)