Identifiying predictive factors of diabetic ketoac...
The article “Identification of Predictive Factors of Diabetic Ketoacidosis in Type 1 Diabetes Using a Subgroup Discovery Algorithm” was written...
The article “Identification of Predictive Factors of Diabetic Ketoacidosis in Type 1 Diabetes Using a Subgroup Discovery Algorithm” was written...
Q-Finder, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associ...
In this paper, we present the Q-Finder algorithm that aims to generate statistically credible subgroups to answer clinical questions, such as findi...
Type 2 diabetes mellitus (T2DM) is a common and heterogeneous disease. Using advanced analytic approaches to explore real-world data may identify d...
IDMPS is an international, observational study to assess care practices and clinical profiles of people with diabetes in developing countries.
Diabetic ketoacidosis (DKA) is a serious complication of type 1 diabetes (T1D), which is difficult to diagnose due to variability in symptoms. Bett...
Limited data exist on the impact of different insulin therapy regimes on outcomes in people with diabetes. The current study describes the unmet me...
To investigate the effects of sodium-glucose co-transporter-2 (SGLT2) inhibitors vs. dipeptidyl peptidase-4 (DPP-4) inhibitors on renal function pr...
This study aimed to evaluate renal function preservation (RFP) of SGLT2i vs DPP4i in Japanese type 2 diabetes patients and identify patient sub-gro...
Detecting consistent patterns of interest can be performed using data-driven subgroup discovery algorithms. These may be instrumental in exploiting...