Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Journal of Biomedical Informatics

Volumn 54, Issue , 2015, Pages 96-105

  Tran, Truyen ^a,b   Nguyen, Tu Dinh ^a   Phung, Dinh ^a   Venkatesh, Svetha ^a  

^a DEAKIN UNIVERSITY   (Australia)

^b CURTIN UNIVERSITY   (Australia)

Author keywords

Electronic medical records; Feature grouping; Medical objects embedding; Suicide risk stratification; Vector representation

Indexed keywords

EMBEDDINGS; HEALTH RISKS; MEDICAL COMPUTING; SUPPORT VECTOR MACHINES; VECTOR SPACES; VECTORS;

ELECTRONIC MEDICAL RECORD; FEATURE GROUPING; MEDICAL OBJECTS EMBEDDING; RISK STRATIFICATION; VECTOR REPRESENTATIONS;

RISK ASSESSMENT;

ARTICLE; ARTIFICIAL NEURAL NETWORK; ELECTRONIC MEDICAL RECORD; HIGH RISK POPULATION; HUMAN; LEARNING; MAJOR CLINICAL STUDY; MENTAL HEALTH; PRIORITY JOURNAL; RESTRICTED BOLTZMANN MACHINE; RISK ASSESSMENT; STRATIFICATION; SUICIDE; SUPPORT VECTOR MACHINE; FEMALE; MALE; MEDICAL INFORMATICS; MENTAL DISORDERS; NEOPLASMS; PROBABILITY; PROCEDURES; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

ELECTRONIC HEALTH RECORDS; FEMALE; HUMANS; MALE; MARKOV CHAINS; MEDICAL INFORMATICS; MENTAL DISORDERS; MODELS, STATISTICAL; NEOPLASMS; NEURAL NETWORKS (COMPUTER); RISK ASSESSMENT; SUICIDE; SUPPORT VECTOR MACHINE;

EID: 84927945601     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2015.01.012     Document Type: Article

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