Machine learning approach to extract diagnostic and prognostic thresholds: Application in prognosis of cardiovascular mortality

Computational and Mathematical Methods in Medicine

Volumn 2012, Issue , 2012, Pages

  Mena, Luis J ^a   Orozco, Eber E ^a   Felix, Vanessa G ^a   Ostos, Rodolfo ^a   Melgarejo, Jesus ^b   Maestre, Gladys E ^b,c  

^a Polytechnic University of Sinaloa   (Mexico)

^b UNIVERSIDAD DEL ZULIA   (Venezuela)

^c Vagelos College of Physicians and Surgeons   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL RESEARCH; DIAGNOSIS; TURING MACHINES;

CARDIO-VASCULAR DISEASE; CARDIO-VASCULAR RISK FACTORS; CARDIOVASCULAR MORTALITY; INNOVATIVE APPROACHES; MACHINE LEARNING APPROACHES; MACHINE LEARNING METHODS; MEDICAL DECISION MAKING; SYMBOLIC CLASSIFICATIONS;

MACHINE LEARNING;

AGED; ARTICLE; BLOOD PRESSURE MONITORING; CARDIOVASCULAR MORTALITY; CARDIOVASCULAR RISK; DIAGNOSTIC ACCURACY; FEMALE; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; OUTCOME ASSESSMENT; PROFESSIONAL KNOWLEDGE; PROGNOSIS; RISK FACTOR; ALGORITHM; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; CARDIOVASCULAR DISEASE; CARDIOVASCULAR SYSTEM; COMPUTER PROGRAM; COMPUTER SIMULATION; INSTRUMENTATION; METHODOLOGY; MIDDLE AGED; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING;

AGED; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BLOOD PRESSURE MONITORING, AMBULATORY; CARDIOVASCULAR DISEASES; CARDIOVASCULAR SYSTEM; COMPUTER SIMULATION; FEMALE; HUMANS; MALE; MIDDLE AGED; MODELS, CARDIOVASCULAR; PROGNOSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE;

EID: 84866161254     PISSN: 1748670X     EISSN: 17486718     Source Type: Journal    
DOI: 10.1155/2012/750151     Document Type: Article

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