Using Machine Learning to Examine Medication Adherence Thresholds and Risk of Hospitalization

Medical Care

Volumn 53, Issue 8, 2015, Pages 720-728

  Lo Ciganic, Wei Hsuan ^a,b   Donohue, Julie M ^a,c   Thorpe, Joshua M ^a,d   Perera, Subashan ^c,e   Thorpe, Carolyn T ^a,d   Marcum, Zachary A ^a,e,f   Gellad, Walid F ^a,d,e  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

^c Graduate School of Public Health   (United States)

^d VETERANS AFFAIRS MEDICAL CENTER   (United States)

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

^f UNIVERSITY OF WASHINGTON   (United States)

Author keywords

classification and regression tree; diabetes; machine learning; medication adherence; survival tree

Indexed keywords

2,4 THIAZOLIDINEDIONE DERIVATIVE; BIGUANIDE; INSULIN; SULFONYLUREA DERIVATIVE; ANTIDIABETIC AGENT;

ADULT; ALGORITHM; ARTICLE; AVOIDANCE BEHAVIOR; COHORT ANALYSIS; CONTROLLED STUDY; DIABETES MELLITUS; DISEASE SEVERITY; ELIXHAUSER COMORBIDITY INDEX; EMERGENCY WARD; FEMALE; HEALTH STATUS; HOSPITALIZATION; HUMAN; LONGITUDINAL STUDY; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAID; MEDICATION COMPLIANCE; NON INSULIN DEPENDENT DIABETES MELLITUS; OUTCOME ASSESSMENT; PREDICTION; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL; RETROSPECTIVE STUDY; RISK FACTOR; TRAINING; AGED; ARTIFICIAL INTELLIGENCE; DIABETES MELLITUS, TYPE 2; MIDDLE AGED; MULTIVARIATE ANALYSIS; ORGANIZATION AND MANAGEMENT; PATIENT COMPLIANCE; RISK ASSESSMENT; STATISTICS AND NUMERICAL DATA; TOTAL QUALITY MANAGEMENT; UNITED STATES; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; ARTIFICIAL INTELLIGENCE; COHORT STUDIES; DIABETES MELLITUS, TYPE 2; FEMALE; HOSPITALIZATION; HUMANS; HYPOGLYCEMIC AGENTS; MALE; MEDICAID; MEDICATION ADHERENCE; MIDDLE AGED; MULTIVARIATE ANALYSIS; PATIENT COMPLIANCE; QUALITY IMPROVEMENT; RETROSPECTIVE STUDIES; RISK ASSESSMENT; UNITED STATES;

EID: 84937913920     PISSN: 00257079     EISSN: 15371948     Source Type: Journal    
DOI: 10.1097/MLR.0000000000000394     Document Type: Article

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