AIMC Topic: Female

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Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.

PloS one
Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent ...

Development and validation of a short form for the Raven's Coloured Progressive Matrices using a machine learning approach.

The British journal of developmental psychology
Raven's Coloured Progressive Matrices (CPM) is a widely used assessment tool for measuring general cognitive ability in developmental and educational research, particularly in studies involving young children. However, administering the full set of t...

High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning.

Animal models and experimental medicine
BACKGROUND: Quantifying the rich home-cage activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models. However, due to the lack of effective behavioral methods, most efforts on tree shrew be...

Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study.

International journal of medical informatics
BACKGROUND: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and vali...

Breast radiation therapy fluence painting with multi-agent deep reinforcement learning.

Medical physics
BACKGROUND: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, the manual fluence painting process presents a challenge for efficient clinical operation.

Application of elastic net for clinical outcome prediction and classification in progressive supranuclear palsy: A multicenter cohort study.

Parkinsonism & related disorders
BACKGROUND: Previous studies have used machine learning to identify clinically relevant atrophic regions in progressive supranuclear palsy (PSP). This study applied Elastic Net (EN) in PSP to uncover key atrophic patterns, offering a novel approach t...

Novel machine learning applications in peripartum care: a scoping review.

American journal of obstetrics & gynecology MFM
OBJECTIVE: Machine learning (ML), a subtype of artificial intelligence (AI), presents predictive modeling and dynamic diagnostic tools to facilitate early interventions and improve decision-making. Considering the global challenges of maternal, fetal...

Exploring the association between personality traits and colour saturation preference using machine learning.

Acta psychologica
Both personality traits and colour saturation are associated with emotion; however, how colour saturation preference interacts with different traits and whether this interaction is modulated by object-colour relations remains unclear. In this study, ...

Assessment of glymphatic function and white matter integrity in children with autism using multi-parametric MRI and machine learning.

European radiology
OBJECTIVES: To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combined with machine learning to evaluate ASD detection performance.

Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems.

Circulation. Genomic and precision medicine
BACKGROUND: While universal screening for Lipoprotein(a) [Lp(a)] is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), a ...