Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.
Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Arti...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 9; pp. 1 - 21 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125068544&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125068544 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2017 vid: 41 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125068544 125068544 125068544 10.1007/s10916-017-0788-2 125068544 ppf: 1 ppct: 20 formats: fmt: @attributes: type: P tig: atl: Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods. aug: au: Hidalgo, J. Ignacio Colmenar, J. Manuel Kronberger, Gabriel Winkler, Stephan M. Garnica, Oscar Lanchares, Juan affil: Adaptive and Bioinspired System Group, School of Informatics , Universidad Complutense de Madrid , C/ Profesor José García Santesmases 9 28040 Madrid Spain sug: subj: Databases, Health Blood Glucose Analysis Insulin Blood Food Intake Diabetic Patients Education Health Knowledge Diabetes Mellitus Complications Access to Information Glycemic Control Blood Glucose Monitoring Algorithms Evaluation Artificial Organs Pancreas Anatomy and Histology Regression Genetics Bioinformatics Data Analysis Software Graphics Descriptive Statistics Human Funding Source ab: Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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