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Volumn 12, Issue , 2012, Pages 131-
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Decision tree-based learning to predict patient controlled analgesia consumption and readjustment.
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Author keywords
[No Author keywords available]
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Indexed keywords
AGE;
ALGORITHM;
ANALGESIA;
ANALYSIS OF VARIANCE;
ARTICLE;
ARTIFICIAL INTELLIGENCE;
ARTIFICIAL NEURAL NETWORK;
BLOOD PRESSURE;
CLASSIFICATION;
COMPARATIVE STUDY;
DECISION TREE;
DRUG ADMINISTRATION;
FEMALE;
HEART RATE;
HUMAN;
INSTRUMENTATION;
MALE;
PATIENT CONTROLLED ANALGESIA;
PHYSIOLOGY;
POSTOPERATIVE PAIN;
PREDICTIVE VALUE;
RETROSPECTIVE STUDY;
RISK FACTOR;
SOCIOECONOMICS;
STANDARD;
TREATMENT OUTCOME;
UNITED STATES;
AGE FACTORS;
ALGORITHMS;
ANALGESIA, PATIENT-CONTROLLED;
ANALYSIS OF VARIANCE;
ARTIFICIAL INTELLIGENCE;
BLOOD PRESSURE;
CHICAGO;
DECISION TREES;
DRUG ADMINISTRATION SCHEDULE;
FEMALE;
HEART RATE;
HUMANS;
MALE;
NEURAL NETWORKS (COMPUTER);
OUTCOME AND PROCESS ASSESSMENT (HEALTH CARE);
PAIN MANAGEMENT;
PAIN, POSTOPERATIVE;
PREDICTIVE VALUE OF TESTS;
RETROSPECTIVE STUDIES;
RISK FACTORS;
SOCIOECONOMIC FACTORS;
MLCS;
MLOWN;
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EID: 84868702962
PISSN: None
EISSN: 14726947
Source Type: Journal
DOI: 10.1186/1472-6947-12-131 Document Type: Article |
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Times cited : (43)
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References (0)
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