Limestone: High-throughput candidate phenotype generation via tensor factorization

Journal of Biomedical Informatics

Volumn 52, Issue , 2014, Pages 199-211

  Ho, Joyce C ^a   Ghosh, Joydeep ^a   Steinhubl, Steve R ^b   Stewart, Walter F ^c   Denny, Joshua C ^d   Malin, Bradley A ^d,e   Sun, Jimeng ^f  

^a The University of Texas at Austin   (United States)

^b SCRIPPS HEALTH   (United States)

^c SUTTER HEALTH   (United States)

^d VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^e VANDERBILT UNIVERSITY   (United States)

^f Georgia Institute of Technology   (United States)

Author keywords

Dimensionality reduction; EHR phenotyping; Nonnegative tensor factorization

Indexed keywords

CLINICAL RESEARCH; DECISION MAKING; DIMENSIONALITY REDUCTION; FACTORIZATION; LIME; LIMESTONE; TENSORS;

CLINICAL RESEARCHERS; ELECTRONIC HEALTH RECORD (EHRS); HEALTHCARE ORGANIZATIONS; HETEROGENEOUS SOURCES; NONNEGATIVE TENSOR FACTORIZATIONS; PHENOTYPING; RECEIVER OPERATOR CHARACTERISTIC CURVES; TENSOR FACTORIZATION;

DIAGNOSIS;

AREA UNDER THE CURVE; ARTICLE; CHEMOMETRICS; CONTROLLED STUDY; DATA MINING; ELECTROENCEPHALOGRAM; ELECTRONIC MEDICAL RECORD; HEART FAILURE; HUMAN; ICD-9; MEDICAL EXPERT; MEDICAL INFORMATICS; POISSON DISTRIBUTION; QUALITATIVE ANALYSIS; ALGORITHM; CLASSIFICATION; FACTUAL DATABASE; PHENOTYPE; PROCEDURES;

ALGORITHMS; DATA MINING; DATABASES, FACTUAL; ELECTRONIC HEALTH RECORDS; HUMANS; PHENOTYPE;

EID: 84919839072     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2014.07.001     Document Type: Article

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