Computational prediction models for early detection of risk of cardiovascular events using mass spectrometry data

IEEE Transactions on Information Technology in Biomedicine

Volumn 12, Issue 5, 2008, Pages 636-643

  Pham, Tuan D ^a   Wang, Honghui ^b   Zhou, Xiaobo ^c   Beck, Dominik ^a   Brandl, Miriam ^a   Hoehn, Gerard ^b   Azok, Joseph ^b   Brennan, Marie Luise ^d   Hazen, Stanley L ^d   Li, King ^a   Wong, Stephen T C ^c  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d LERNER RESEARCH INSTITUTE   (United States)

Author keywords

Cardiovascular risk; Early disease detection; Mass spectrometry (MS); Prediction models; Proteomics

Indexed keywords

DISEASE CONTROL; FEATURE EXTRACTION; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; MASS SPECTROMETERS; MASS SPECTROMETRY; MATHEMATICAL MODELS; SPECTROMETERS; SPECTROMETRY; SPECTRUM ANALYSIS; STATISTICAL TESTS;

CARDIOVASCULAR RISK; EARLY DISEASE DETECTION; MASS SPECTROMETRY (MS); PREDICTION MODELS; PROTEOMICS;

STATISTICAL METHODS;

BIOLOGICAL MARKER; PLASMA PROTEIN;

ALGORITHM; ARTICLE; BLOOD; BLOOD ANALYSIS; CARDIOVASCULAR DISEASE; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; HUMAN; MASS SPECTROMETRY; METHODOLOGY; REPRODUCIBILITY; RISK ASSESSMENT; RISK FACTOR; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BIOLOGICAL MARKERS; BLOOD CHEMICAL ANALYSIS; BLOOD PROTEINS; CARDIOVASCULAR DISEASES; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; MASS SPECTROMETRY; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; SENSITIVITY AND SPECIFICITY;

EID: 51749121574     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2007.908756     Document Type: Article

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