Improving diagnostic recognition of primary hyperparathyroidism with machine learning

Surgery (United States)

Volumn 161, Issue 4, 2017, Pages 1113-1121

  Somnay, Yash R ^a   Craven, Mark ^a   McCoy, Kelly L ^b   Carty, Sally E ^b   Wang, Tracy S ^c   Greenberg, Caprice C ^a   Schneider, David F ^a  

^a UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^c MEDICAL COLLEGE OF WISCONSIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CALCIUM PHOSPHATE; CREATININE; PARATHYROID HORMONE; VITAMIN D;

ADULT; AGE; AREA UNDER THE CURVE; ARTICLE; BAYESIAN LEARNING; CALCIUM BLOOD LEVEL; CLINICAL STUDY; COHORT ANALYSIS; CONTROLLED STUDY; CREATININE BLOOD LEVEL; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; HIGH VOLUME HOSPITAL; HUMAN; INSTRUMENT VALIDATION; LEARNING ALGORITHM; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; PARATHYROID HORMONE BLOOD LEVEL; PARATHYROIDECTOMY; PREDICTIVE VALUE; PREOPERATIVE EVALUATION; PRIMARY HYPERPARATHYROIDISM; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SENSITIVITY ANALYSIS; SEX DIFFERENCE; THYROIDECTOMY; VITAMIN BLOOD LEVEL; ALGORITHM; BAYES THEOREM; BLOOD; CASE CONTROL STUDY; FACTUAL DATABASE; HYPERPARATHYROIDISM, PRIMARY; PROCEDURES; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX; TOTAL QUALITY MANAGEMENT;

ALGORITHMS; BAYES THEOREM; CASE-CONTROL STUDIES; DATABASES, FACTUAL; FEMALE; HUMANS; HYPERPARATHYROIDISM, PRIMARY; MACHINE LEARNING; MALE; MIDDLE AGED; PARATHYROID HORMONE; PARATHYROIDECTOMY; PREDICTIVE VALUE OF TESTS; QUALITY IMPROVEMENT; ROC CURVE; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX;

EID: 85008429707     PISSN: 00396060     EISSN: 15327361     Source Type: Journal    
DOI: 10.1016/j.surg.2016.09.044     Document Type: Article

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