Blar i forfatter "Albers, David"
-
Data-driven blood glucose pattern classification and anomalies detection: Machine-learning applications in Type 1 diabetes
Woldaregay, Ashenafi Zebene; Årsand, Eirik; Botsis, Taxiarchis; Albers, David; Mamykina, Lena; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-05-01)<p><i>Background - </i>Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading ... -
Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes
Woldaregay, Ashenafi Zebene; Årsand, Eirik; Walderhaug, Ståle; Albers, David; Mamykina, Lena; Botsis, Taxiarchis; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-07-26)<i>Background</i>: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabetes. However, self-management of the disease, especially keeping BG in the recommended range, is central to the treatment. This includes actively ... -
A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism
Woldaregay, Ashenafi Zebene; Launonen, Ilkka Kalervo; Albers, David; Igual, Jorge; Årsand, Eirik; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-08-12)<i>Background</i>: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged ... -
Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System
Woldaregay, Ashenafi Zebene; Launonen, Ilkka Kalervo; Årsand, Eirik; Albers, David; Holubova, Anna; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-08-12)<i>Background</i>: Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key ...