Aid decision algorithms to estimate the risk in congenital heart surgery

Computer Methods and Programs in Biomedicine

Volumn 126, Issue , 2016, Pages 118-127

  Ruiz Fernández, Daniel ^a   Monsalve Torra, Ana ^a   Soriano Payá, Antonio ^a   Marín Alonso, Oscar ^a   Triana Palencia, Eddy ^b  

^a UNIVERSITY OF ALICANTE   (Spain)

^b Paediatric Cardiovascular Surgery Department of Cardiovascular Foundation of Colombia   (Colombia)

Author keywords

Artificial neural networks; Classifiers; Congenital heart disease; Data analysis; Decision trees

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CARDIOLOGY; CLASSIFIERS; COMPLEX NETWORKS; CONFORMAL MAPPING; DATA REDUCTION; DECISION SUPPORT SYSTEMS; DECISION TREES; DISEASES; FORESTRY; HEART; IMAGE SEGMENTATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MULTILAYERS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; RISK PERCEPTION; SELF ORGANIZING MAPS; SURGERY; TREES (MATHEMATICS);

CLINICAL DECISION SUPPORT SYSTEMS; CONGENITAL HEART DISEASE; DECISION ALGORITHMS; DECISION SUPPORTS; HIGH COMPLEXITY; MEDIUM COMPLEXITY; PRIORI ESTIMATION; SURGICAL OUTCOME;

ARTIFICIAL HEART;

ACCURACY; ADOLESCENT; ADULT; ARTICLE; ARTIFICIAL INTELLIGENCE; CHILD; CLASSIFICATION ALGORITHM; CLINICAL STUDY; CONGENITAL HEART DISEASE; DECISION SUPPORT SYSTEM; DECISION TREE; DISEASE CLASSIFICATION; FEMALE; GROUPS BY AGE; HEART SURGERY; HUMAN; INFANT; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; OUTCOME ASSESSMENT; PERCEPTRON; SURGICAL RISK; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CARDIOLOGY; CLINICAL DECISION MAKING; HEART DEFECTS, CONGENITAL; NEWBORN; PROCEDURES; REPRODUCIBILITY; RISK; STATISTICAL MODEL;

ALGORITHMS; CARDIAC SURGICAL PROCEDURES; CARDIOLOGY; CLINICAL DECISION-MAKING; DECISION SUPPORT TECHNIQUES; DECISION TREES; FEMALE; HEART DEFECTS, CONGENITAL; HUMANS; INFANT; INFANT, NEWBORN; LIKELIHOOD FUNCTIONS; MACHINE LEARNING; MALE; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; RISK;

EID: 84960091250     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2015.12.021     Document Type: Article

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