Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models.
Glucose concentration in type 1 diabetes is a function of biological and environmental factors which present high inter-patient variability. The objective of this study is to evaluate a number of features, which are extracted from medical and lifestyle self-monitoring data, with respect to their abi...
| Published in: | Medical & Biological Engineering & Computing Vol. 53; no. 12; pp. 1305 - 1319 |
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| Main Authors: | , , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Dec2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=111243903&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111243903 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2015 vid: 53 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 111243903 111243903 NLM25773366 111243903 10.1007/s11517-015-1263-1 NLM25773366 111243903 ppf: 1305 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models. aug: au: Georga, Eleni Protopappas, Vasilios Polyzos, Demosthenes Fotiadis, Dimitrios Georga, Eleni I Protopappas, Vasilios C Fotiadis, Dimitrios I affil: Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina Greece sug: subj: Blood Glucose Analysis Diabetes Mellitus, Type 1 Blood Models, Statistical Insulin Pharmacodynamics Regression Hypoglycemic Agents Pharmacodynamics Female Hypoglycemic Agents Therapeutic Use Middle Age Insulin Therapeutic Use Algorithms Adult Male Blood Glucose Drug Effects Human Middle Aged: 45-64 years Adult: 19-44 years Female Male ab: Glucose concentration in type 1 diabetes is a function of biological and environmental factors which present high inter-patient variability. The objective of this study is to evaluate a number of features, which are extracted from medical and lifestyle self-monitoring data, with respect to their ability to predict the short-term subcutaneous (s.c.) glucose concentration of an individual. Random forests (RF) and RReliefF algorithms are first employed to rank the candidate feature set. Then, a forward selection procedure follows to build a glucose predictive model, where features are sequentially added to it in decreasing order of importance. Predictions are performed using support vector regression or Gaussian processes. The proposed method is validated on a dataset of 15 type diabetics in real-life conditions. The s.c. glucose profile along with time of the day and plasma insulin concentration are systematically highly ranked, while the effect of food intake and physical activity varies considerably among patients. Moreover, the average prediction error converges in less than d/2 iterations (d is the number of features). Our results suggest that RF and RReliefF can find the most informative features and can be successfully used to customize the input of glucose models. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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