Endocrinology

Latest AI and machine learning research in endocrinology for healthcare professionals.

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Large language model-assisted causal machine learning for identifying fatigue-related poor glycated hemoglobin in type 2 diabetes

Fatigue is common but mostly untreated in type 2 diabetes, since it requires a diagnostic workup which is hardly justified by fatigue alone. Individually identifying fatigue-related cause help decide effective follow-up and prioritize resource utilization. This study aimed to determine whether glycated hemoglobin (HbA1c) level contributes to fatigue for each individual case using a fatigue predict...

Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk

Biological aging clocks across organs and omics data, including clinical phenotypes, neuroimaging, proteomics, and epigenetics, have proven instrumental in advancing our understanding of human aging and disease. Here, we expand this aging clock framework to plasma metabolomics by developing 5 organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derived metabolites fro...

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders

Biomarker research in psychopathology increasingly employs high-dimensional omics approaches. Yet, proteomics based on human hair remain largely unexp...

Genetic Architecture and Risk Prediction of Gestational Diabetes Mellitus in over 116,144 Chinese Pregnancies

Gestational diabetes mellitus (GDM), a heritable metabolic disorder and the most common pregnancy-related condition, remains understudied regarding it...

In-context learning for data-efficient classification of diabetic retinopathy with multimodal foundation models

In-context learning, a prompt-based learning mechanism that enables multimodal foundation models to adapt to new tasks, can eliminate the need for ret...

A Combined Predictive and Causal Approach for Neighborhood-Level Diabetes Detection

Develop a neighborhood-level framework using machine learning and causal inference to identify socioeconomic and behavioral drivers of Type 2 diabetes...

Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality

Spine age estimated from lateral spine radiographs and DXA vertebral fracture assessments (VFAs) could be associated with fracture and mortality risk....

Profile of deaths mentioning ischemic and hemorrhagic stroke in Brazil: a population-based machine learning analysis

Brazil has the highest stroke rates in Latin America. The aim of this study was to investigate the profile of deaths mentioning stroke in Brazil betwe...

Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients

This study aimed to evaluate the impact of socioeconomic disparities on the allocation of second-line treatments among patients with type 2 diabetes (...

Deep learning clarifies association of osteoporosis risk with bone metastasis in premenopausal women after surgery for early-stage breast cancer: a multicenter retrospective cohort study

Adjuvant use of bone-modifying agents (BMAs) to early-stage breast cancer (eBC) aims to maintain bone density, leading to prevention of bone metastasi...

Episode-Driven Insights: Can Large Language Models Tackle Multimodal Diabetes Data?

This study explores the potential of state-of-the-art large language models (LLM) to scaffold type 1 diabetes management by automating the analysis of...

GlucoseGo: A Simple, User-Friendly, Machine Learning-Derived Tool for Predicting Exercise-Related Hypoglycaemia Risk in Type 1 Diabetes

This study aims to develop an accessible, machine learning-derived tool for people with type 1 diabetes that predicts hypoglycaemia risk at the start ...

Machine learning-Based Classification of Papillary Thyroid Carcinoma Versus Multinodular Goiter Using Preoperative Laboratory and Cytology Data

Thyroid nodules are frequently encountered in clinical practice, with their detection increasing due to advancements in imaging modalities. While most...

Evaluation of Machine Learning Models for Early Prediction of Gestational Diabetes Using Retrospective Electronic Health Records from Current and Previous Pregnancies

To assess the performance of machine learning (ML) models in predicting gestational diabetes mellitus (GDM) using electronic health record (EHR) data ...

Developing a GraphRAG-enabled local-LLM for Gestational Diabetes Mellitus

This paper re-imagines a world of abundance in the treatment of chronic diseases such as Tpe 2 Diabetes. It asks: what if preventive and diagnostic re...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting

This study aimed to identify prognostic factors associated with poor outcomes of COVID-19 at diagnosis in Primary Health Care (PHC). We conducted a re...

Nocturnal Glycemic Stability Index

The Nocturnal Glycemic Stability Index (NGSI) is a novel quantitative metric designed to comprehensively assess overnight glucose stability by integra...

RFA-U-Net: A Foundation Model-Driven Approach for Accurate Choroid Segmentation in OCT Imaging

The choroid layer plays a critical role in maintaining outer retinal health and is implicated in numerous vision-threatening diseases such as diabetic...

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s Disease

Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progressive cognitive impairment, behavioral changes, and ...

Regulatory risk loci link disrupted androgen response to pathophysiology of Polycystic Ovary Syndrome

A major challenge in deciphering the complex genetic landscape of Polycystic Ovary Syndrome (PCOS) lies in the limited understanding of how susceptibi...

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