Obesity

Characterization Of Diseases In Temporal Comorbidity Networks

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used pub...

The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion

We introduce the Aging Multiverse, a framework for generating multiple plausible facial aging trajectories from a single image, each conditioned on external factors such as environment, health, and lifestyle. Unlike prior methods that model aging as a single deterministic path, our approach creates an aging tree that visualizes diverse futures. To enable this, we propose a training-free diffusio...

Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis.

With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantati...

A Model-Mediated Stacked Ensemble Approach for Depression Prediction Among Professionals

Depression is a significant mental health concern, particularly in professional environments where work-related stress, financial pressure, and life...

Development of an explainable machine learning model for predicting depression in adolescent girls with non-suicidal self-injury: A cross-sectional multicenter study.

Non-suicidal self-injury (NSSI) in adolescent girls is a critical predictor of subsequent depression and suicide risk, yet current tools lack both acc...

Evaluation of machine-learning models to measure individualized treatment effects from randomized clinical trial data with time-to-event outcomes

In randomized clinical trials, regression models can be used to explore the relationships between patients' variables (e.g., clinical, pathological ...

Diabetes Prediction and Management Using Machine Learning Approaches

Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines t...

PulseRide: A Robotic Wheelchair for Personalized Exertion Control with Human-in-the-Loop Reinforcement Learning

Maintaining an active lifestyle is vital for quality of life, yet challenging for wheelchair users. For instance, powered wheelchairs face increasin...

TestAgent: An Adaptive and Intelligent Expert for Human Assessment

Accurately assessing internal human states is key to understanding preferences, offering personalized services, and identifying challenges in real-w...

Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods.

Mental health disorders like depression, anxiety, and stress (DAS) are rising globally. Understanding how diet and lifestyle influence these condition...

An Ultra-Low Power Wearable BMI System With Continual Learning Capabilities.

Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Br...

Enhancing osteoporosis risk prediction using machine learning: A holistic approach integrating biomarkers and clinical data.

Osteoporosis (OP) affects approximately 18 % of the global population, with osteoporosis-associated fractures impacting up to 37 million people annual...

NLP for computational insights into nutritional impacts on colorectal cancer care.

Colorectal cancer (CRC) is one of the most prominent cancers globally, with its incidence rising among younger adults due to improved screening practi...

Visceral Fat Quantified by a Fully Automated Deep-Learning Algorithm and Risk of Incident and Recurrent Diverticulitis.

BACKGROUND: Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdomina...

Eigenhearts: Cardiac diseases classification using eigenfaces approach.

In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses....

Identifying the key predictors of positive self-perceptions of aging using machine learning.

This study aimed to identify key predictors of self-perceptions of aging (SPA) among older adults by examining a comprehensive set of potential predic...

Machine learning-based prediction of hearing loss: Findings of the US NHANES from 2003 to 2018.

The prevalence of hearing loss (HL) has emerged as an escalating public health concern globally. The objective of this study was to leverage data from...

LaMP-QA: A Benchmark for Personalized Long-form Question Answering

Personalization is essential for question answering systems that are user-centric. Despite its importance, personalization in answer generation has ...

Application of machine learning algorithms in osteoporosis analysis based on cardiovascular health assessed by life's essential 8: a cross-sectional study.

BACKGROUND: Life's Essential 8 (LE8) for assessing cardiovascular health (CVH) has been demonstrated to be inversely associated with osteoporosis (OP)...

Research on multi-algorithm and explainable AI techniques for predictive modeling of acute spinal cord injury using multimodal data.

Machine learning technology has been extensively applied in the medical field, particularly in the context of disease prediction and patient rehabilit...