[HTML][HTML] Machine learning and data mining methods in diabetes research

I Kavakiotis, O Tsave, A Salifoglou… - Computational and …, 2017 - Elsevier
The remarkable advances in biotechnology and health sciences have led to a significant
production of data, such as high throughput genetic data and clinical information, generated …

Development and validation of a machine learning model using administrative health data to predict onset of type 2 diabetes

M Ravaut, V Harish, H Sadeghi, KK Leung… - JAMA network …, 2021 - jamanetwork.com
Importance Systems-level barriers to diabetes care could be improved with population
health planning tools that accurately discriminate between high-and low-risk groups to guide …

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test

HT Abbas, L Alic, M Erraguntla, JX Ji, M Abdul-Ghani… - Plos one, 2019 - journals.plos.org
Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle
modifications and drug intervention can prevent diabetes, therefore, an early identification of …

Predictive biomarkers for type 2 of diabetes mellitus: Bridging the gap between systems research and personalized medicine

C Kraniotou, V Karadima, G Bellos, GT Tsangaris - Journal of proteomics, 2018 - Elsevier
The global incidence of metabolic disorders like type 2 diabetes mellitus (DM2) has
assumed epidemic proportions, leading to adverse health and socio-economic impacts. It is …

[HTML][HTML] The effect of short-term intensive insulin therapy in newly-diagnosed Type-2 diabetic patients

C Karacaer, T Demirci, H Cengiz, C Varim… - Pakistan Journal of …, 2021 - ncbi.nlm.nih.gov
Objectives: We aimed to determine the effect of short-term intensive insulin therapy (SIIT) on
long-term glycemic control in newly-diagnosed Type-2 diabetes mellitus (nT2DM) patients …

Patient characteristics are not associated with clinically important differential response to dapagliflozin: a staged analysis of phase 3 data

S Bujac, A Del Parigi, J Sugg, S Grandy, T Liptrot… - Diabetes Therapy, 2014 - Springer
Introduction This study aimed to determine if data mining methodologies could identify
reproducible predictors of dapagliflozin-specific treatment response in the phase 3 clinical …

Strikes and Gutters: Biomarkers and anthropometric measures for predicting diagnosed diabetes mellitus in adults in low-and middle-income countries

SS Simmons - Heliyon, 2023 - cell.com
Background The management of diabetes necessitates the requirement of reliable health
indices, specifically biomarkers and anthropometric measures, to detect the presence or …

NCEP-ATP III and IDF criteria for metabolic syndrome predict type 2 diabetes mellitus

E Sulistiowati, M Sihombing - Universa Medicina, 2016 - univmed.org
Background Subjects with metabolic syndrome (MetS) have a greater risk for acquiring type
2 diabetes mellitus (type 2 DM). The MetS criteria usually used are those of the National …

Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management

A Lim, A Singh, J Chiam, C Eckert, V Kumar… - arXiv preprint arXiv …, 2021 - arxiv.org
Prediction of diabetes and its various complications has been studied in a number of
settings, but a comprehensive overview of problem setting for diabetes prediction and care …

Precision Medicine: Viable Pathways to Address Existing Research Gaps

KK Pegues - 2018 - tigerprints.clemson.edu
Precision Medicine (PM) seeks to customize medical treatments for patients based on
measurable and identifiable characteristics. Unlike personalized medicine, this effort is not …