Researchers developed a machine learning model that could identify children in the ED who were at risk for developing sepsis ...
Background We investigated the prevalence, temporal trends and associated factors of overweight and obesity among adults in ...
A machine learning lung cancer risk prediction model outperformed logistic regression, supporting improved risk assessment and more efficient radiology based lung cancer screening.
Objective: To understand patient portal engagement stratified by patient characteristics among adults 50 years and older with at least 1 common chronic medical condition using electronic health ...
Introduction We aimed to determine the association between paternal labour migration and the growth of the left-behind ...
Objectives Although breastfeeding is associated with lower postnatal depression and anxiety, limited research exists regarding long-term maternal mental health outcomes. This study examined the ...
That challenge is examined in the study Towards Eco-Friendly Cybersecurity: Machine Learning-Based Anomaly Detection with ...
Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
Aims This study aimed to explore ocular and systemic factors associated with pathologic myopia in patients with high myopia ...
Google Ads quietly rolls out a powerful new AI model that is better able to catch policy violations and malicious activity.
Background Ebstein’s anomaly (EA) exhibits significant anatomical and clinical heterogeneity, warranting a systematic ...
Food-insecure individuals have fewer total annual visits (in-person and via telehealth) across 4 types of office-based and outpatient visits: general checkup, diagnosis or treatment, psychotherapy or ...
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