Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning.
Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict co...
| Published in: | Health Informatics Journal Vol. 26; no. 1; pp. 703 - 719 |
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| Main Authors: | , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
Mar2020
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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=143231244&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143231244 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Mar2020 vid: 26 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 143231244 143231244 143231244 10.1177/1460458219850682 143231244 ppf: 703 ppct: 16 formats: tig: atl: Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning. aug: au: Vehí, Josep Contreras, Iván Oviedo, Silvia Biagi, Lyvia Bertachi, Arthur affil: Universitat de Girona, Spain sug: subj: Diabetes Mellitus, Type 1 Hypoglycemia Prevention and Control Hypoglycemia Diagnosis Machine Learning Methods Risk Assessment Risk Management Human Hypoglycemia Risk Factors Glycemic Control Diabetic Patients Psychosocial Factors Quality of Life Decision Support Systems, Clinical Patient Safety Adverse Health Care Event Algorithms Blood Glucose Monitoring Vectorcardiography Neural Networks (Computer) Data Mining Exploratory Research Correlational Studies Sensitivity and Specificity Engineering ab: Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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