A Bayesian Nonparametric Model for Disease Subtyping: Application to Emphysema Phenotypes

IEEE Transactions on Medical Imaging

Volumn 36, Issue 1, 2017, Pages 343-354

  Ross, James C ^a   Castaldi, Peter J ^a   Cho, Michael H ^a   Chen, Junxiang ^b   Chang, Yale ^b   Dy, Jennifer G ^b   Silverman, Edwin K ^a   Washko, George R ^a   Jose Estepar, Raul San ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b Northeastern University   (United States)

Author keywords

Bayes methods; computed tomography; knowledge discovery; machine learning; pattern analysis; prediction methods

Indexed keywords

DATA MINING; FORMAL LANGUAGES; LEARNING SYSTEMS; MEDICAL IMAGING; POPULATION STATISTICS; PULMONARY DISEASES; TISSUE; TISSUE ENGINEERING;

BAYES METHOD; BAYESIAN NONPARAMETRIC MODELING; CHRONIC OBSTRUCTIVE PULMONARY DISEASE; CROSS-VALIDATION ANALYSIS; ORDINARY LEAST SQUARES REGRESSIONS; PATTERN ANALYSIS; PREDICTION METHODS; SINGLE NUCLEOTIDE POLYMORPHISMS;

COMPUTERIZED TOMOGRAPHY;

AGE; ALGORITHM; ARTICLE; BAYES THEOREM; CHRONIC OBSTRUCTIVE LUNG DISEASE; CONTROLLED STUDY; EMPHYSEMA; ENTROPY; ERROR; GENETIC ANALYSIS; GENETIC ASSOCIATION; HISTOGRAM; HUMAN; LUNG DENSITY; MAJOR CLINICAL STUDY; MARKOV CHAIN; MEAN SQUARED ERROR; PREDICTION; QUANTITATIVE ANALYSIS; RESPIRATORY TRACT PARAMETERS; SINGLE NUCLEOTIDE POLYMORPHISM; SMOKING; X-RAY COMPUTED TOMOGRAPHY; LUNG EMPHYSEMA; PHENOTYPE;

BAYES THEOREM; HUMANS; PHENOTYPE; PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; PULMONARY EMPHYSEMA; SMOKING; TOMOGRAPHY, X-RAY COMPUTED;

EID: 85009072612     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2016.2608782     Document Type: Article

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