Blood glucose prediction using stochastic modeling in neonatal intensive care

IEEE Transactions on Biomedical Engineering

Volumn 57, Issue 3, 2010, Pages 509-518

  Le Compte, Aaron J ^a   Lee, Dominic S ^a   Chase, J Geoffrey ^a   Lin, Jessica ^a   Lynn, Adrienne ^b   Shaw, Geoffrey M ^c  

^a UNIVERSITY OF CANTERBURY   (New Zealand)

^b CHRISTCHURCH WOMEN S HOSPITAL   (New Zealand)

^c CHRISTCHURCH HOSPITAL   (New Zealand)

Author keywords

Forecasting; Human factors; Stochastic approximation

Indexed keywords

BLOOD GLUCOSE; CLINICAL DATA; CONDITIONAL PROBABILITIES; CROSS VALIDATION; GESTATIONAL AGE; GLUCOSE CONTROL; HUMAN FACTORS; INCREASED SAFETY; INSULIN SENSITIVITY; INTENSIVE CARE; KERNEL MODELS; NEONATAL INTENSIVE CARE; OPTIMAL COVERAGE; PROBABILITY FORECASTS; REGULATORY SYSTEMS; STOCHASTIC APPROXIMATION; STOCHASTIC APPROXIMATIONS; STOCHASTIC MODELING; TIME VARYING; VARIANCE ESTIMATORS;

APPROXIMATION THEORY; BLOOD; ESTIMATION; FORECASTING; GLUCOSE; HUMAN ENGINEERING; INSULIN; PEDIATRICS; REAL TIME CONTROL; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC SYSTEMS;

STOCHASTIC MODELS;

GLUCOSE;

ARTICLE; CLINICAL ARTICLE; CONTROLLED STUDY; DYNAMICS; FORECASTING; GESTATIONAL AGE; GLUCOSE BLOOD LEVEL; GLYCEMIC CONTROL; HUMAN; HYPOGLYCEMIA; INFANT; INSULIN SENSITIVITY; INSULIN TREATMENT; KERNEL METHOD; NEWBORN; NEWBORN INTENSIVE CARE; PREDICTION; PROBABILITY; RETROSPECTIVE STUDY; STOCHASTIC MODEL; VARIANCE;

ALGORITHMS; BLOOD GLUCOSE; COHORT STUDIES; HUMANS; INFANT, NEWBORN; INFANT, PREMATURE; INFANT, VERY LOW BIRTH WEIGHT; INTENSIVE CARE, NEONATAL; MODELS, BIOLOGICAL; PREDICTIVE VALUE OF TESTS; STOCHASTIC PROCESSES;

EID: 77649139839     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2009.2035517     Document Type: Article

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