{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T14:25:43Z","timestamp":1788877543700,"version":"build-2803163510"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research, Development and Innovation Fund of Hungary","award":["Project TKP2020-IKA-07, 2020-4.1.1-TKP2020 Thematic Excellence Programme 2020 - Institutional Excellence sub-program funding scheme"],"award-info":[{"award-number":["Project TKP2020-IKA-07, 2020-4.1.1-TKP2020 Thematic Excellence Programme 2020 - Institutional Excellence sub-program funding scheme"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: The daily lifestyle management of diabetes requires accurate predictions of the blood glucose level between meals. The objective of this study was to improve the accuracy achieved by previous work, especially on the mid-term, i.e., 120 to 180 min prediction horizons, for insulin-dependent patients. Materials and Methods: An absorption model-based method is proposed to train an artificial neural network with the bolus and basal insulin dosing and timing, the baseline blood glucose level, the maximal glucose infusion rate, and the total carbohydrate content as parameters. The approach was implemented in various algorithmic setups, and it was validated on data from a small-scale clinical trial with continuous glucose monitoring. Results: Root mean square error results for the mid-term horizons are 1.72 mmol\/L (120 min) and 1.95 mmol\/L (180 min). The accuracy of the proposed model measured on the clinical data is better than the accuracy reported by any other currently available and comparable models. Conclusions: A relatively short (ca. two weeks) training sample of a continuous glucose monitor and dietary\/insulin log is sufficient to provide accurate predictions. For the outpatient application in practice, a hybrid model is proposed that combines the present mid-term method with the authors\u2019 previous work for short-term predictions.<\/jats:p>","DOI":"10.3390\/medicina57070676","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T10:03:19Z","timestamp":1625047399000},"page":"676","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improved Methods for Mid-Term Blood Glucose Level Prediction Using Dietary and Insulin Logs"],"prefix":"10.3390","volume":"57","author":[{"given":"Rebaz A. H.","family":"Karim","sequence":"first","affiliation":[{"name":"Medical Informatics Research & Development Center, University of Pannonia, 8200 Veszpr\u00e9m, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9833-8864","authenticated-orcid":false,"given":"Istv\u00e1n","family":"Vass\u00e1nyi","sequence":"additional","affiliation":[{"name":"Medical Informatics Research & Development Center, University of Pannonia, 8200 Veszpr\u00e9m, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Istv\u00e1n","family":"K\u00f3sa","sequence":"additional","affiliation":[{"name":"Cardiac Rehabilitation Institute of the Military Hospital, 8230 Balatonf\u00fcred, Hungary"},{"name":"Department of Preventive Medicine, University of Szeged, 6700 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107843","DOI":"10.1016\/j.diabres.2019.107843","article-title":"Global and Regional Diabetes Prevalence Estimates for 2019 and Projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th Edition","volume":"157","author":"Saeedi","year":"2019","journal-title":"Diabetes Res. 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