Performance of a machine learning classifier of knee mri reports in two large academic radiology practices: A tool to estimate diagnostic yield

American Journal of Roentgenology

Volumn 208, Issue 4, 2017, Pages 750-753

  Hassanpour, Saeed ^a   Langlotz, Curtis P ^a   Amrhein, Timothy J ^b   Befera, Nicholas T ^b   Lungren, Matthew P ^a  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

Classification; Machine learning; MRI

Indexed keywords

ACCURACY; ARTICLE; BOOTSTRAPPING; CLASSIFICATION; CLASSIFIER; DIAGNOSTIC TEST ACCURACY STUDY; DIAGNOSTIC VALUE; HUMAN; KNEE; MACHINE LEARNING; MEDICAL PRACTICE; NATURAL LANGUAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; RECALL; RETROSPECTIVE STUDY; SOFTWARE; SUPPORT VECTOR MACHINE; AUTOMATED PATTERN RECOGNITION; CALIFORNIA; CLINICAL TRIAL; DATA MINING; DIAGNOSTIC IMAGING; IMAGE ENHANCEMENT; MULTICENTER STUDY; NORTH CAROLINA; ORGANIZATION AND MANAGEMENT; PROCEDURES; RADIOLOGY DEPARTMENT; RADIOLOGY INFORMATION SYSTEM; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICS AND NUMERICAL DATA; UNIVERSITY HOSPITAL; WORKLOAD;

ACADEMIC MEDICAL CENTERS; CALIFORNIA; DATA MINING; HUMANS; IMAGE ENHANCEMENT; KNEE; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; NATURAL LANGUAGE PROCESSING; NORTH CAROLINA; PATTERN RECOGNITION, AUTOMATED; RADIOLOGY DEPARTMENT, HOSPITAL; RADIOLOGY INFORMATION SYSTEMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; WORKLOAD;

EID: 85016515607     PISSN: 0361803X     EISSN: 15463141     Source Type: Journal    
DOI: 10.2214/AJR.16.16128     Document Type: Article

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