Diabetes

Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach.

OBJECTIVE: To identify independent risk factors for diabetic peripheral neuropathic pain (DPNP), construct a nomogram prediction model, and quantify the contribution of predictive factors using SHapley Additive exPlanations (SHAP) values. METHODS: This retrospective study of 500 type 2 diabetes patients diagnosed DPNP via the Michigan Neuropathy Screening Instrument and clinical evaluation. Predic...

Can ensemble methods improve predictive performance of existing models estimating chronic kidney disease among patients with diabetes?

BACKGROUND: Clinical prediction models often suffer from poor model transportability and/or subgroup performance resulting from using a single data source. We aimed to determine whether ensemble methods can combine multiple existing models to improve predictive performance when compared to component models. METHODS: As a case study, we used electronic medical records from the Canadian Primary Care...

Deep learning-driven MRI segmentation of choroid plexus volume: a novel biomarker for cognitive impairment in type 2 diabetes mellitus.

Palmatine ameliorates MASLD in type 2 diabetes by modulating hepatic apoptosis and inflammation.

Type 2 diabetes mellitus (T2DM) is a major risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), and their convergence pre...

Feature reduction using swarm optimization and random forest classifiers for early diabetes risk prediction.

Diabetes is a chronic metabolic disorder caused by excessive blood sugar levels, which leads to severe damage to other organs. Type 2 diabetes is, mor...

Epidemiology of Peripheral Artery Disease, Risk Factors, and Trends.

Peripheral artery disease (PAD) affects more than 230 million people worldwide, with a disproportionate burden in low- and middle-income countries. PA...

Predicting the Timing of the Metabolic Inflection Point in Type 1 Diabetes Progression Using Machine Learning and Survival Analysis Models.

We undertook this study to improve early identification of the metabolic inflection point (IP) preceding clinical type 1 diabetes in autoantibody-posi...

Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy.

OBJECTIVES: Based on ultrasound technology and clinical indicators, this study intends to develop multiple risk prediction models for diabetic periphe...

Artificial intelligence in mobile health applications: A comprehensive review of its role in diabetes care.

This review explores the integration of artificial intelligence (AI) in mobile health applications for diabetes care. It focuses on key AI methodologi...

Integrated machine learning and multi-omics analysis identifies ALOX5 as a potential therapeutic target for tubulointerstitial inflammation in diabetic kidney disease.

Diabetic kidney disease (DKD) is a leading cause of renal failure. Inflammation of the renal tubules and interstitium is a critical factor in the prog...

Behavioural, cognitive, and computational risk factors for type 2 diabetes: A systematic review.

BACKGROUND: Type 2 diabetes mellitus represents a global public health challenge, with rising prevalence driven by complex interactions between lifest...

Advances in Electrocardiogram-Based Non-Invasive Blood Glucose Monitoring Technology.

Blood glucose monitoring is fundamental to diabetes management, yet traditional invasive methods are limited by patient discomfort and infection risks...

Artificial Intelligence in Type 1 Diabetes Management: A Scoping Review of Randomised Controlled Trials.

BACKGROUND: Artificial intelligence is emerging in healthcare systems. In type 1 diabetes, AI-enabled tools are increasingly used to support nutrition...

Graph-Enhanced Multi-Task Learning for Type 2 Diabetes Comorbidity Risk Prediction.

Early prediction of Type 2 diabetes mellitus (T2DM) complications holds significant clinical importance for improving patient outcomes and reducing he...

Personalized Glucose Management With AI: Pilot Study Using a Multiarmed Bandit Approach.

BACKGROUND: Personalized behavioral recommendations through mobile apps have proven effective in preventing serious chronic diseases such as diabetes....

TCB-Kla: a hybrid deep learning model for predicting human lysine lactylation sites by incorporating transformer encoder and multi-scale CNN.

Lysine lactylation as a newly discovered post-translational modification of proteins, plays a key role in various cellular processes. It can be stimul...

Associations between 12 insulin resistance surrogates with metabolic dysfunction-associated steatotic liver disease risk and all-cause mortality: data from the NHANES III (1988-1994).

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) has been shown to be intimately linked to the presence of insulin resista...

Identification and validation of NETs-associated biomarkers in osteoporosis with diabetes.

Osteoporosis and diabetes represent major global public health challenges. Neutrophil extracellular traps (NETs) serve as key components of the innate...

Development of machine learning-based predictive models for seroma formation after transabdominal preperitoneal inguinal hernia repair.

BACKGROUND: Inguinal hernia repair, particularly transabdominal preperitoneal (TAPP) repair, is a common surgical procedure. However, seroma formation...

Detection of Non-diabetic Kidney Disease in Patients with Diabetes Using Machine Learning and Electronic Medical Record Data.

INTRODUCTION: The identification of non-diabetic kidney disease (NDKD) in diabetic patients is critically important. Unlike diabetic nephropathy, NDKD...