Search Articles

View query in Help articles search

Search Results (1 to 2 of 2 Results)

Download search results: CSV END BibTex RIS


Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial

Recorded rates of diabetes among ethnically diverse middle-aged people with a diagnosis of established psychosis in South London reach 20%, with a further 30% evidencing raised blood sugar levels (dysglycemia) [7]. Likewise, the prevalence of both diabetes and dysglycemia is higher in inpatient psychiatric settings than in the general community [8].

Dipen Patel, Yamiko Joseph Msosa, Tao Wang, Julie Williams, Omar G Mustafa, Siobhan Gee, Barbara Arroyo, Damian Larkin, Trevor Tiedt, Angus Roberts, Richard J B Dobson, Fiona Gaughran

JMIR Res Protoc 2024;13:e49548

Machine Learning Prediction of Hypoglycemia and Hyperglycemia From Electronic Health Records: Algorithm Development and Validation

Machine Learning Prediction of Hypoglycemia and Hyperglycemia From Electronic Health Records: Algorithm Development and Validation

The association between hypoglycemia and hyperglycemia and poor outcomes in inpatients who are critically and noncritically ill calls for a rigorous inpatient management approach toward reducing dysglycemia [13,14]. The environment of inpatients (ie, a hospital setting) is usually well-controlled; nevertheless, the maintenance of BG levels in a normoglycemic range is demanding.

Harald Witte, Christos Nakas, Lia Bally, Alexander Benedikt Leichtle

JMIR Form Res 2022;6(7):e36176