Assessment of type II diabetes mellitus using irregularly sampled measurements with missing data

Bioprocess and Biosystems Engineering

Volumn 38, Issue 4, 2015, Pages 615-629

  Barazandegan, Melissa ^a   Ekram, Fatemeh ^a   Kwok, Ezra ^a   Gopaluni, Bhushan ^a   Tulsyan, Aditya ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b Massachusetts Institute of Technology Cambridge   (United States)

Author keywords

Bayesian framework; Online identification; SIR particle filtering; Type II diabetes mellitus

Indexed keywords

BLOOD; DYNAMICS; GLUCOSE; INSULIN; MONTE CARLO METHODS; PATIENT TREATMENT; SOCIAL NETWORKING (ONLINE); STATE SPACE METHODS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

BAYESIAN FRAMEWORKS; INSULIN CONCENTRATIONS; NON-CLINICAL SETTINGS; NONLINEAR STATE SPACE MODELS; ON-LINE IDENTIFICATION; PARTICLE FILTERING; SEQUENTIAL MONTE CARLO; TYPE II DIABETES MELLITUS;

PARAMETER ESTIMATION;

GLUCOSE; INCRETIN; INSULIN; GLUCOSE BLOOD LEVEL;

ARTICLE; BAYES THEOREM; CLINICAL ASSESSMENT; DATA ANALYSIS; DIABETIC PATIENT; GLUCOSE BLOOD LEVEL; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MEASUREMENT; NON INSULIN DEPENDENT DIABETES MELLITUS; PREDICTIVE VALUE; PRIORITY JOURNAL; ANALYSIS; BLOOD; CLINICAL TRIAL (TOPIC); COMPUTER SIMULATION; DIABETES MELLITUS, TYPE 2; HUMAN; MONTE CARLO METHOD; PROBABILITY; STATISTICS; THEORETICAL MODEL; TISSUE DISTRIBUTION;

BAYES THEOREM; BLOOD GLUCOSE; CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; DIABETES MELLITUS, TYPE 2; HUMANS; INCRETINS; INSULIN; MODELS, THEORETICAL; MONTE CARLO METHOD; PREDICTIVE VALUE OF TESTS; PROBABILITY; STOCHASTIC PROCESSES; TISSUE DISTRIBUTION;

EID: 84928646834     PISSN: 16157591     EISSN: 16157605     Source Type: Journal    
DOI: 10.1007/s00449-014-1301-7     Document Type: Article

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