Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

PLoS ONE

Volumn 8, Issue 6, 2013, Pages

  Lasko, Thomas A ^a   Denny, Joshua C ^a   Levy, Mia A ^a,b  

^a VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b VANDERBILT INGRAM CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

URIC ACID;

ARTICLE; BAYESIAN LEARNING; CLINICAL STUDY; COMPUTATIONAL PHENOTYPE DISCOVERY; ELECTRONIC MEDICAL RECORD; ENVIRONMENTAL FACTOR; FACE VALIDITY; GENE MUTATION; GENETIC ASSOCIATION; GOLD STANDARD; HUMAN; INFORMATION PROCESSING; LEARNING ALGORITHM; MATHEMATICAL MODEL; NORMAL DISTRIBUTION; PHENOTYPIC VARIATION; PROCESS OPTIMIZATION; UNSUPERVISED FEATURE LEARNING; URIC ACID BLOOD LEVEL;

HUMANS; LEARNING; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; PHENOTYPE;

EID: 84879468407     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0066341     Document Type: Article

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