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Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data.

Optometry and vision science : official publication of the American Academy of Optometry
SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncycloplegic clinical data. A classification model predicted post-cycloplegic myopia and could be useful i...

SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis.

International journal of molecular sciences
Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular mechanisms hindering effective therapeutic development. This study identified SERPINA3 as a potential...

Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.

Journal of Korean medical science
BACKGROUND: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize...

The feasibility of using generative artificial intelligence for history taking in virtual patients.

BMC research notes
OBJECTIVE: This study aimed to design and develop a virtual patient program using generative Artificial Intelligence (AI) technology, providing medical students opportunities to practice history-taking with a chatbot. We evaluated the feasibility of ...

Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.

BMC medical informatics and decision making
BACKGROUND: Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream infections. SAB exhibits a high recurrence rate and mortality rate, leading to numerous clinical tre...

How adaptive social robots influence cognitive, emotional, and self-regulated learning.

Scientific reports
As educational environments become more diverse, adaptive technologies like social robots hold promise for providing individual support to learners. This study investigated the role of adaptive teaching of a robot on students' learning outcomes, emot...

Cognitive performance classification of older patients using machine learning and electronic medical records.

Scientific reports
Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improvin...

Validation of a machine learning model for indirect screening of suicidal ideation in the general population.

Scientific reports
Suicide is among the leading causes of death worldwide and a concerning public health problem, accounting for over 700,000 registered deaths worldwide. However, suicide deaths are preventable with timely and evidence-based interventions, which are of...

An assessment of machine learning methods to quantify blood lactate from neutrophils phagocytic activity.

Scientific reports
Phagocytosis is a critical component of innate immunity that helps the body defend itself against infection, foreign particles, and cellular debris. Investigating and quantifying phagocytosis can help understand how the immune system identifies forei...