AIMC Topic: Diabetes Mellitus

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Integrating artificial intelligence in community-based diabetes care programmes: enhancing inclusiveness, diversity, equity and accessibility a realist review protocol.

BMJ open
INTRODUCTION: Marginalised populations-such as racialised groups, low-income individuals, newcomers and those in rural areas-disproportionately experience severe diabetes-related complications, including diabetic foot ulcers, retinopathy and amputati...

Enhancing diabetes risk prediction through focal active learning and machine learning models.

PloS one
To improve the effectiveness of diabetes risk prediction, this study proposes a novel method based on focal active learning strategies combined with machine learning models. Existing machine learning models often suffer from poor performance on imbal...

Social and Structural Determinants of Lower Extremity Amputations in Diabetes.

Current diabetes reports
PURPOSE OF REVIEW: Lower extremity amputations (LEAs) are among the most severe complications of diabetes, with approximately 1.5 million procedures performed globally each year. This review explores the impact of social and structural determinants o...

Application of IRSA-BP neural network in diagnosing diabetes.

PloS one
Within the healthcare sector, the application of machine learning is gaining prominence, notably enhancing the efficiency and precision of diagnostic procedures. This study focuses on this key area of diabetes prediction and aims to develop an innova...

Prediction of 30-day readmission in diabetes management using Machine learning.

Computers in biology and medicine
This study aims to develop a robust and accurate model to forecast 30-day readmissions for patients with diabetes by leveraging machine learning techniques. Diabetes, being a chronic condition with complex care needs, often leads to frequent hospital...

Multi-datasets transfer multitask learning for simultaneous blood glucose and blood pressure monitoring using common PPG features.

Computers in biology and medicine
The simultaneous monitoring of both blood glucose level (BGL) and blood pressure (BP) has rarely been studied directly. The exploitation of physiological interactions between them will advance the learning of either task. However, the lack of availab...

High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning Identification.

The journal of physical chemistry letters
Accurate and early detection of C-peptide, a stable biomarker indicative of diabetes, is crucial for disease diagnosis, treatment, and prevention. This study explores a novel detection methodology using solid-state nanopore technology coupled with ma...

KEM-IoMT: Knowledge graph embedding-enhanced accurate medical service recommendation against diabetes.

Computers in biology and medicine
The Internet of Medical Things (IoMT)-enhanced Recommender System (RS) acquired swift advancement in configuring diverse medical data into intelligent systems to generate personalized medical services. However, due to the heterogeneous and complex na...

Chemical Properties-Based Deep Learning Models for Recommending Rational Daily Diet Combinations to Diabetics Through Large-Scale Virtual Screening of α-Glucosidase Dietary-Derived Inhibitors and Verified In Vitro.

Journal of agricultural and food chemistry
The lack of suitable chemical research methodologies has hindered the discovery of rational daily diet combinations from large-scale dietary-derived compounds. Three deep learning models based on chemical properties for α-glucosidase inhibitors (AGIs...

Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.

Scientific reports
Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy, neuropathy, foot problems, heart attacks, and strokes if left unchecked. Prompt detection and diagn...