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Supervised and Unsupervised Approaches for the Real-Time Detection of Undesired Insulin Suspension Caused by Malfunctions.
J Diabetes Sci Technol. 2024 Apr 29:19322968241248402. doi: 10.1177/19322968241248402. Online ahead of print.
J Diabetes Sci Technol. 2024.
PMID: 38682800
Free PMC article.
Data-Driven Supervised Compression Artifacts Detection on Continuous Glucose Sensors.
Idi E, Manzoni E, Sparacino G, Del Favero S.
Idi E, et al.
Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1145-1148. doi: 10.1109/EMBC48229.2022.9870884.
Annu Int Conf IEEE Eng Med Biol Soc. 2022.
PMID: 36085641
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Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study.
Guazzo A, Atzeni M, Idi E, Trescato I, Tavazzi E, Longato E, Manera U, Chió A, Gromicho M, Alves I, de Carvalho M, Vettoretti M, Di Camillo B.
Guazzo A, et al. Among authors: idi e.
BMC Med Inform Decis Mak. 2024 Oct 29;24(Suppl 4):318. doi: 10.1186/s12911-024-02719-5.
BMC Med Inform Decis Mak. 2024.
PMID: 39472842
Free PMC article.
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Unsupervised Retrospective Detection of Pressure Induced Failures in Continuous Glucose Monitoring Sensors for T1D Management.
Idi E, Manzoni E, Facchinetti A, Sparacino G, Favero SD.
Idi E, et al.
IEEE J Biomed Health Inform. 2024 Sep 20;PP. doi: 10.1109/JBHI.2024.3465893. Online ahead of print.
IEEE J Biomed Health Inform. 2024.
PMID: 39302774
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