Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain

Pain Medicine (United States)

Volumn 16, Issue 7, 2015, Pages 1386-1401

  Tighe, Patrick J ^a,e   Harle, Christopher A ^b,e   Hurley, Robert W ^a,e   Aytug, Haldun ^c,e   Boezaart, Andre P ^a,e   Fillingim, Roger B ^d,e  

^a UNIVERSITY OF FLORIDA COLLEGE OF MEDICINE   (United States)

^b Department of Health Services Research Management and Policy College of Public Health and Health Professions   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

^d UNIVERSITY OF FLORIDA COLLEGE OF DENTISTRY   (United States)

^e MEDICAL COLLEGE OF WISCONSIN   (United States)

Author keywords

Algorithm; Machine Learning; Pain Prediction; Postoperative Pain

Indexed keywords

ACUTE POSTOPERATIVE PAIN; ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; COHORT ANALYSIS; DIAGNOSTIC TEST ACCURACY STUDY; FEASIBILITY STUDY; FEMALE; GRADIENT BOOSTED DECISION TREE ALGORITHM; HUMAN; INTERMETHOD COMPARISON; K NEAREST NEIGHBOR; LEARNING ALGORITHM; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR ALGORITHM; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEASUREMENT ACCURACY; MIDDLE AGED; PAIN SEVERITY; POSTOPERATIVE PAIN; PREDICTION; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; ALGORITHM; DECISION TREE; PAIN, POSTOPERATIVE; STATISTICAL MODEL;

ALGORITHMS; DECISION TREES; HUMANS; LOGISTIC MODELS; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); PAIN, POSTOPERATIVE; RETROSPECTIVE STUDIES; ROC CURVE;

EID: 84947133089     PISSN: 15262375     EISSN: 15264637     Source Type: Journal    
DOI: 10.1111/pme.12713     Document Type: Article

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