AI Medical Compendium Journal:
Diabetes & metabolic syndrome

Showing 1 to 10 of 35 articles

Machine learning algorithms mimicking specialists decision making on initial treatment for people with type 2 diabetes mellitus in Japan diabetes data management study (JDDM76).

Diabetes & metabolic syndrome
OBJECTIVE: To evaluate whether typical machine learning models that mimic specialists' care can successfully reproduce information, not only on whether to prescribe medications but also which hypoglycemic agents to prescribe as initial treatment for ...

Machine learning and statistical models to predict all-cause mortality in type 2 diabetes: Results from the UK Biobank study.

Diabetes & metabolic syndrome
AIMS: This study aims to compare the performance of contemporary machine learning models with statistical models in predicting all-cause mortality in patients with type 2 diabetes mellitus and to develop a user-friendly mortality risk prediction tool...

Obesity prediction: Novel machine learning insights into waist circumference accuracy.

Diabetes & metabolic syndrome
AIMS: This study aims to enhance the precision of obesity risk assessments by improving the accuracy of waist circumference predictions using machine learning techniques.

Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms.

Diabetes & metabolic syndrome
INTRODUCTION: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40% of patient...

A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance.

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic revi...

Artificial intelligence facial recognition system for diagnosis of endocrine and metabolic syndromes based on a facial image database.

Diabetes & metabolic syndrome
AIM: To build a facial image database and to explore the diagnostic efficacy and influencing factors of the artificial intelligence-based facial recognition (AI-FR) system for multiple endocrine and metabolic syndromes.

Exploring the potential of ChatGPT in the peer review process: An observational study.

Diabetes & metabolic syndrome
BACKGROUND: Peer review is the established method for evaluating the quality and validity of research manuscripts in scholarly publishing. However, scientific peer review faces challenges as the volume of submitted research has steadily increased in ...

The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets.

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid n...

ChatGPT: Is this version good for healthcare and research?

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: There have been advancements in artificial intelligence (AI) and deep learning in the past decade. Recently, OpenAI Inc. has launched a new chatbot, called ChatGPT that interacts in a conversational way and its dialogue format ma...

Artificial intelligence and body composition.

Diabetes & metabolic syndrome
AIMS: Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease i...