Diabetes

A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study.

BACKGROUND: Building machine learning models that are interpretable, explainable, and fair is critical for their trustworthiness in clinical practice. Interpretability, which refers to how easily a human can comprehend the mechanism by which a model makes predictions, is often seen as a primary consideration when adopting a machine learning model in health care. However, interpretability alone doe...

Integrative Machine Learning Approach for Predicting Resistance to First-Generation Receptor Ligands in Acromegaly.

CONTEXT: Acromegaly, caused by excess growth hormone (GH) and insulin-like growth factor-1 (IGF-1) due to pituitary adenomas, often necessitates first-generation somatostatin receptor ligands (fgSRLs) therapy when surgery fails. However, responses to fgSRLs therapy vary widely.

In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.

Drug-induced cardiotoxicity poses a significant risk to human health, and reliable predictive models are needed for safety assessment. In this study, ...

On the usage of artificial intelligence in leprosy care: A systematic literature review.

Leprosy, or Hansen's disease, is a Neglected Tropical Disease (NTD) caused by Mycobacterium leprae that mainly affects the skin and peripheral nerves,...

Topo-CNN: Retinal Image Analysis with Topological Deep Learning.

The analysis of fundus images is vital for early detection of retinal diseases such as diabetic retinopathy (DR), glaucoma, and age-related macular de...

Machine learning prediction of intestinal α-glucosidase inhibitors using a diverse set of ligands: a drug repurposing effort with drugBank database screening.

UNLABELLED: The global rise in diabetes mellitus (DM) poses a significant health challenge, necessitating effective therapeutic interventions. α-Gluco...

Application of IRSA-BP neural network in diagnosing diabetes.

Within the healthcare sector, the application of machine learning is gaining prominence, notably enhancing the efficiency and precision of diagnostic ...

Personalized machine learning models for noninvasive hypoglycemia detection in people with type 1 diabetes using a smartwatch: Insights into feature importance during waking and sleeping times.

Hypoglycemia is a major challenge for people with diabetes. Therefore, glycemic monitoring is an important aspect of diabetes management. However, cur...

Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction.

Precise forecasting of cancer outcomes is essential for medical professionals to assess the well-being of patients and develop customized therapeutic ...

Big data and cardiovascular risk-insights into obesity, diabetes, and coronary heart disease.

BACKGROUND: Cardiovascular diseases (CVD) remain a major global health burden. Obesity and type 2 diabetes mellitus (T2DM) are key modifiable risk fac...

Identification of hub genes involved in the pathogenesis of diabetic nephropathy: A multi-omics study integrating machine learning, mendelian randomization and mediation analysis.

BACKGROUND: Diabetic nephropathy (DN), affecting 30%-40% of diabetic patients, is the leading cause of end-stage renal disease worldwide. This study a...

Lipidomic profiling of human adiposomes identifies specific lipid shifts linked to obesity and cardiometabolic risk.

BACKGROUNDObesity, a growing health concern, often leads to metabolic disturbances, systemic inflammation, and vascular dysfunction. Emerging evidence...

Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps.

Nasal polyps (NP) are benign mucosal outgrowths associated with chronic inflammation that can significantly reduce quality of life. This study aimed t...

Machine learning-based QSAR and molecular modeling identify promising PTP1B modulators from Ocimum gratissimum for type 2 diabetes therapy.

Protein tyrosine phosphatase 1B (PTP1B) is a key negative regulator of insulin signaling and a promising therapeutic target for the treatment of type ...

Association rule mining of the human gut microbiome.

The human gut carries a vast and diverse microbial community that is essential for human health. Understanding the structure of this complex community...

Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.

OBJECTIVE: IgA nephropathy (IgAN) is the most common form of glomerulonephritis worldwide, characterized by immune complex deposition in the glomerula...

Effectiveness and safety of AI-driven closed-loop systems in diabetes management: a systematic review and meta-analysis.

BACKGROUND: Diabetes is a metabolic disease that can lead to severe cardiovascular diseases and neuropathy. The associated medical costs and complicat...

Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus.

BACKGROUND: Diabetes Mellitus (DM) is a global health concern with rising prevalence, and its link to PFAS exposure remains unclear. No machine learni...

Intervention through an intelligent technological platform for socio-emotional development and health promotion in adolescents with Type 1 Diabetes Mellitus (emoTICare): A study protocol for randomized controlled trial.

BACKGROUND: During adolescence, individuals face various challenges that require adequate psychosocial and emotional adjustment. Additionally, the pre...

Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.

Sepsis secondary to urinary tract infection after kidney stone surgery is associated with considerable morbidity. Limited research examines the use o...