{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T12:18:54Z","timestamp":1790079534957,"version":"4.0.1"},"reference-count":0,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2009,4,1]],"date-time":"2009-04-01T00:00:00Z","timestamp":1238544000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diabetes Technology &amp; Therapeutics"],"published-print":{"date-parts":[[2009,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Estimation of future glucose concentrations is a crucial task for diabetes management. Predicted glucose values can be used for early hypoglycemic\/hyperglycemic alarms or for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Continuous glucose monitoring (CGM) technologies provide glucose readings at a high frequency and consequently detailed insight into the subject's glucose variations. The objective of this research is to develop reliable subject-specific glucose prediction models using CGM data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Two separate patient databases collected under hospitalized (disturbance-free) and normal daily life conditions are used for validation of the proposed glucose prediction algorithm. Both databases consist of glucose concentration data collected at 5-min intervals using a CGM device. Using time-series analysis, low-order linear models are developed from patients' own CGM data. The time-series models are integrated with recursive identification and change detection methods, which enables dynamic adaptation of the model to inter-\/intra-subject variability and glycemic disturbances. Prediction performance is evaluated in terms of glucose prediction error and Clarke Error Grid analysis (CG-EGA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Prediction errors are significantly reduced with recursive identification of the models, and predictions are further improved with inclusion of a parameter change detection method. CG-EGA analysis results in accurate readings of 90% or more.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Subject-specific glucose prediction strategy has been developed. Including a change detection method to the recursive algorithm improves the prediction accuracy. The proposed modeling algorithm with small number of parameters is a good candidate for installation in portable devices for early hypoglycemic\/hyperglycemic alarms and for closing the glucose regulation loop with an insulin pump.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/dia.2008.0065","type":"journal-article","created":{"date-parts":[[2009,4,3]],"date-time":"2009-04-03T08:10:48Z","timestamp":1238746248000},"page":"243-253","source":"Crossref","is-referenced-by-count":128,"title":["Estimation of Future Glucose Concentrations with Subject-Specific Recursive Linear Models"],"prefix":"10.1177","volume":"11","author":[{"given":"Meriyan","family":"Eren-Oruklu","sequence":"first","affiliation":[{"name":"Illinois Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Cinar","sequence":"additional","affiliation":[{"name":"Illinois Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauretta","family":"Quinn","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donald","family":"Smith","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2009,4,1]]},"container-title":["Diabetes Technology &amp; Therapeutics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/dia.2008.0065","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/dia.2008.0065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:16:53Z","timestamp":1777400213000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1089\/dia.2008.0065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,4]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2009,4]]}},"alternative-id":["10.1089\/dia.2008.0065"],"URL":"https:\/\/doi.org\/10.1089\/dia.2008.0065","relation":{},"ISSN":["1520-9156","1557-8593"],"issn-type":[{"value":"1520-9156","type":"print"},{"value":"1557-8593","type":"electronic"}],"subject":[],"published":{"date-parts":[[2009,4]]}}}