Cross-Industry Standard Process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management

Clinical and Translational Oncology

Volumn 14, Issue 1, 2012, Pages 73-79

  Rivo, Eduardo ^a   De La Fuente, Javier ^a   Rivo, Ángel ^a   García Fontán, Eva ^a   Cañizares, Miguel Ángel ^a   Gil, Pedro ^a  

^a UNIVERSITY OF VIGO   (Spain)

Author keywords

CRISP DM; Data mining; Knowledge discovery in databases; Lung cancer; Surgery mortality

Indexed keywords

ADULT; AGED; ARTICLE; CANCER SURGERY; CONTROLLED STUDY; FEMALE; HEALTH CARE QUALITY; HUMAN; LUNG CANCER; LUNG RESECTION; MAJOR CLINICAL STUDY; MALE; MEDICAL DECISION MAKING; METHODOLOGY; MINING; MORTALITY; PERIPHERAL OCCLUSIVE ARTERY DISEASE; PREDICTIVE VALUE; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SURGICAL MORTALITY; TOTAL QUALITY MANAGEMENT;

ADULT; AGED; AGED, 80 AND OVER; DATA MINING; DECISION MAKING; FEMALE; HUMANS; KNOWLEDGE; LOGISTIC MODELS; LUNG NEOPLASMS; MALE; MIDDLE AGED; MODELS, THEORETICAL; PREDICTIVE VALUE OF TESTS; PULMONARY SURGICAL PROCEDURES; QUALITY OF HEALTH CARE; RISK FACTORS; ROC CURVE;

EID: 84860704295     PISSN: 1699048X     EISSN: 16993055     Source Type: Journal    
DOI: 10.1007/s12094-012-0764-8     Document Type: Article

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