AIMC Topic: Diabetes Mellitus, Type 1

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A multimodal physiological dataset for non-invasive blood glucose estimation.

Scientific data
Diabetes is a major health challenge that affects millions of people worldwide. Managing diabetes effectively requires monitoring blood glucose levels continuously, typically through invasive sensing devices such as continuous glucose monitors (CGMs)...

A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.

Scientific reports
Continuous glucose monitoring (CGM) devices allow real-time glucose readings leading to improved glycemic control. However, glucose predictions in the lower (hypoglycemia) and higher (hyperglycemia) extremes, referred as glycemic excursions, remain c...

Performance of several large language models when answering common patient questions about type 1 diabetes in children: accuracy, comprehensibility and practicality.

BMC pediatrics
BACKGROUND: The use of large language models (LLMs) in healthcare has expanded significantly with advances in natural language processing. Models, such as ChatGPT and Google Gemini, are increasingly used to generate human-like responses to questions,...

Multimodal MRI analysis selecting key brain features for machine learning based classification of diabetic neuropathic pain and phenotypes.

Journal of the neurological sciences
Cerebral alterations are associated with diabetic peripheral neuropathy (DPN) and neuropathic pain, including reductions in brain volumes, cortical thickness, sulcus depth, and alterations in metabolites and functional connectivity. This study combin...

Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model.

Scientific reports
Personalized blood glucose (BG) prediction in Type 1 Diabetes (T1D) is challenged by significant inter-patient heterogeneity. To address this, we propose BiT-MAML, a hybrid model combining a Bidirectional LSTM-Transformer with Model-Agnostic Meta-Lea...

Exploration of autophagy-associated genes and potential molecular mechanisms in type 1 diabetes and osteoporosis.

Scientific reports
The co-occurrence of osteoporosis (OP) and type 1 diabetes mellitus (T1DM) represents a clinically significant comorbidity pattern, characterized by skeletal fragility and insulin deficiency. While epidemiological links exist, their shared molecular ...

Urinary Complement proteome strongly linked to diabetic kidney disease progression.

Nature communications
Diabetic kidney disease (DKD) progression is not well understood. Using high-throughput proteomics, biostatistical, pathway and machine learning tools, we examine the urinary Complement proteome in two prospective cohorts with type 1 or 2 diabetes an...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

Journal of diabetes science and technology
BACKGROUND: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longe...

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.

PloS one
Hypoglycemia is a major challenge for people with diabetes. Therefore, glycemic monitoring is an important aspect of diabetes management. However, current methods such as finger pricking and continuous glucose monitoring systems (CGMS) are invasive, ...