Endocrinology

A Comparative Study of Diabetes Prediction Based on Lifestyle Factors Using Machine Learning

Diabetes is a prevalent chronic disease with significant health and economic burdens worldwide. Early prediction and diagnosis can aid in effective management and prevention of complications. This study explores the use of machine learning models to predict diabetes based on lifestyle factors using data from the Behavioral Risk Factor Surveillance System (BRFSS) 2015 survey. The dataset consists...

Fusion of Various Optimization Based Feature Smoothing Methods for Wearable and Non-invasive Blood Glucose Estimation

Recently, the wearable and non-invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. To address this issue, this paper proposes a polynomial fitting approach to smooth...

GlucoLens: Explainable Postprandial Blood Glucose Prediction from Diet and Physical Activity

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after meals, is a critical indicator of progression toward ...

Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations. Current models rely on structu...

MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.

Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that brid...

Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment

Diabetic retinopathy (DR) is a leading cause of vision loss, requiring early and accurate assessment to prevent irreversible damage. Spectral Domain...

AI-Augmented Thyroid Scintigraphy for Robust Classification

Thyroid scintigraphy is a key imaging modality for diagnosing thyroid disorders. Deep learning models for thyroid scintigraphy classification often ...

Two-Stage Deep Learning Model for Adrenal Nodule Detection on CT Images: A Retrospective Study.

Background The detection and classification of adrenal nodules are crucial for their management. Purpose To develop and test a deep learning model to ...

Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.

Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in th...

Personalized Causal Graph Reasoning for LLMs: A Case Study on Dietary Recommendations

Large Language Models (LLMs) effectively leverage common-sense knowledge for general reasoning, yet they struggle with personalized reasoning when t...

RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering

Diabetic Macular Edema (DME), a prevalent complication among diabetic patients, constitutes a major cause of visual impairment and blindness. Althou...

Weakly Supervised Segmentation Framework for Thyroid Nodule Based on High-confidence Labels and High-rationality Losses

Weakly supervised segmentation methods can delineate thyroid nodules in ultrasound images efficiently using training data with coarse labels, but su...

A graph neural network-based multispectral-view learning model for diabetic macular ischemia detection from color fundus photographs

Diabetic macular ischemia (DMI), marked by the loss of retinal capillaries in the macular area, contributes to vision impairment in patients with di...

Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. However, most state-of-the-art image classifiers ba...

Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate

Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avert the dangers of dysgl...

SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical ...

Machine Learning for Everyone: Simplifying Healthcare Analytics with BigQuery ML

Machine learning (ML) transforms healthcare by enabling predictive analytics, personalized treatments, and improved patient outcomes. However, tradi...

KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical ...

Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial ...

PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network.

SUMMARY: Polygenic risk scores (PRSs) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power rema...