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Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.

Ecotoxicology and environmental safety
There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on hea...

Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines.

Theranostics
Diagnosis and treatment efficacy of major depressive disorder (MDD) currently lack stable and reliable biomarkers. Previous research has suggested a potential association between immune cells, cytokines, and the pathophysiology and treatment of MDD....

An interpretable deep learning model for detecting pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images.

PeerJ
BACKGROUND: Determining the status of breast cancer susceptibility genes () is crucial for guiding breast cancer treatment. Nevertheless, the need for genetic testing among breast cancer patients remains unmet due to high costs and limited resources...

Deep learning-based whole-brain B -mapping at 7T.

Magnetic resonance in medicine
PURPOSE: This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B ) maps from multi-slice localizer scans with different slice orientations in the human head at 7T, a...

Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.

ESC heart failure
AIMS: Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs).

Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.

Oral diseases
OBJECTIVES: Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes.

Tapping Into Awareness: Assessing Nursing Students' Water Consumption Behaviors and Sustainability Perceptions Through a Cross-Sectional Study With Machine Learning Approach.

Public health nursing (Boston, Mass.)
INTRODUCTION: Investigating water consumption behaviors and perceptions of water sustainability among nursing students is crucial for effective resource management. This study employs machine learning (ML) techniques to analyze these factors in detai...

A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS-Based Microparticles in a Breast Cancer Model.

ACS nano
Multimodal cancer therapies are often required for progressive cancers due to the high persistence and mortality of the disease and the negative systemic side effects of traditional therapeutic methods. Thus, the development of less invasive modaliti...

Using machine learning methods to identify trajectories of change and predict responders and non-responders to short-term dynamic therapy.

Psychotherapy research : journal of the Society for Psychotherapy Research
Predicting therapy responders can significantly improve clinical outcomes. This study aims to identify predictors of response to short-term dynamic therapy. Data from 95 patients who underwent 16-session therapy were analyzed using machine learning...