Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models

Journal of Biomedical Informatics

Volumn 78, Issue , 2018, Pages 33-42

  Mayhew, Michael B ^a   Petersen, Brenden K ^a   Sales, Ana Paula ^a   Greene, John D ^b   Liu, Vincent X ^b   Wasson, Todd S ^a  

^a LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

^b DIVISION OF RESEARCH   (United States)

Author keywords

Cluster analysis; Composite mixture model; Electronic health records; Mixture modeling; Risk stratification; Sepsis

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DIAGNOSIS; HEALTH RISKS; HOSPITALS; LEARNING SYSTEMS; MEDICAL COMPUTING; MIXTURES; MULTIVARIANT ANALYSIS; PATIENT TREATMENT; RISK ASSESSMENT;

ELECTRONIC HEALTH RECORD; ELECTRONIC MEDICAL RECORD; ELECTRONIC MEDICAL RECORDS (EMRS); MIXTURE MODEL; MULTIVARIATE ANALYSIS TECHNIQUES; RISK STRATIFICATION; SEPSIS; STATISTICAL MACHINE LEARNING;

PHYSIOLOGICAL MODELS;

ARTICLE; CLUSTER ANALYSIS; COHORT ANALYSIS; COMPOSITE MIXTURE MODEL; DATA PROCESSING; ELECTRONIC MEDICAL RECORD; HOSPITAL MORTALITY; MORTALITY RISK; PRIORITY JOURNAL; SEPSIS; STATISTICAL MODEL; TREND STUDY; CLASSIFICATION; ELECTRONIC HEALTH RECORD; FACTUAL DATABASE; HUMAN; RISK; STATISTICS AND NUMERICAL DATA;

CLUSTER ANALYSIS; DATABASES, FACTUAL; ELECTRONIC HEALTH RECORDS; HUMANS; MODELS, STATISTICAL; RISK; SEPSIS;

EID: 85041409711     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2017.11.015     Document Type: Article

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