AI Medical Compendium Topic

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Cross-Sectional Studies

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Effects of Job Crafting and Leisure Crafting on Nurses' Burnout: A Machine Learning-Based Prediction Analysis.

Journal of nursing management
AIM: To explore the status of job crafting, leisure crafting, and burnout among nurses and to examine the impact of job crafting and leisure crafting variations on burnout using machine learning-based models.

The Impact of Medical Explainable Artificial Intelligence on Nurses' Innovation Behaviour: A Structural Equation Modelling Approach.

Journal of nursing management
This study aims to investigate the influence of medical explainable artificial intelligence (XAI) on the innovation behaviour of nurses, as well as explore the dual-pathway mediating effect of AI self-efficacy and AI anxiety and organizational ethic...

Nursing Students' Personality Traits and Their Attitude toward Artificial Intelligence: A Multicenter Cross-Sectional Study.

Journal of nursing management
BACKGROUND: Despite the importance of studying factors contributing to nursing students' attitudes toward artificial intelligence, yet according to our knowledge, no study has addressed the relationship between personality traits and the attitude of ...

Identifying the Influencing Factors of Depressive Symptoms among Nurses in China by Machine Learning: A Multicentre Cross-Sectional Study.

Journal of nursing management
BACKGROUND: Nurses' high workload can result in depressive symptoms. However, the research has underexplored the internal and external variables, such as organisational support, career identity, and burnout, which may predict depressive symptoms amon...

Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective.

Journal of nursing management
Workplace violence, defined as any disruptive behavior or threat to employees, seriously threatens junior nurses. Compared with senior nurses, junior nurses are more vulnerable to workplace violence due to inexperience, low professional recognition,...

Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.

Scientific reports
The precise and efficient diagnosis of an individual's skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations ...

Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.

Scientific reports
Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent and improve prognosis potentially. We aimed to develop and validate an explainable prediction model ...

Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare.

NeuroImage
Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on assessments of cognitive and behavioral skills that develop towards the second year of life and afte...

Fostering creativity-nurturing behaviors among nurse educators: Investigating the interplay between evidence-based practice climate and artificial intelligence competence self-efficacy.

Nurse education today
BACKGROUND: Fostering creativity in nursing education is essential for equipping students with critical thinking and problem-solving skills. Nurse educators play a pivotal role in nurturing creativity among nursing students, yet their effectiveness i...