An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records

Artificial Intelligence in Medicine

Volumn 65, Issue 2, 2015, Pages 155-166

  Kavuluru, Ramakanth ^a,b   Rios, Anthony ^b   Lu, Yuan ^b  

^a UNIVERSITY OF KENTUCKY   (United States)

^b UNIVERSITY OF KENTUCKY   (United States)

Author keywords

Diagnosis code assignment; Label calibration; Learning to rank; Multi label text classification

Indexed keywords

CALIBRATION; CODES (SYMBOLS); COMPUTER AIDED DIAGNOSIS; DATA REDUCTION; DIAGNOSIS; FEATURE EXTRACTION; MEDICAL COMPUTING; MEDICINE; METADATA; RADIATION; RADIOLOGY; SUPERVISED LEARNING; TEXT PROCESSING;

CODE ASSIGNMENTS; ELECTRONIC MEDICAL RECORD; ELECTRONIC MEDICAL RECORDS (EMRS); INTERNATIONAL CLASSIFICATION OF DISEASE; LEARNING TO RANK; MULTI-LABEL TEXT CLASSIFICATION; PROBLEM TRANSFORMATIONS; SUPERVISED LEARNING APPROACHES;

CLASSIFICATION (OF INFORMATION);

ARTICLE; DIAGNOSIS CODE; ELECTRONIC MEDICAL RECORD; HUMAN; INTERNATIONAL CLASSIFICATION OF DISEASES; LEARNING; PRIORITY JOURNAL; RADIOLOGY; UNITED STATES; CLASSIFICATION; DISEASES; ELECTRONIC HEALTH RECORD; EMPIRICAL RESEARCH; INFORMATION PROCESSING;

DATASETS AS TOPIC; DISEASE; ELECTRONIC HEALTH RECORDS; EMPIRICAL RESEARCH; HUMANS;

EID: 84943587337     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2015.04.007     Document Type: Article

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