AIMC Topic: Cross-Sectional Studies
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Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective.
Journal of nursing management
Apr 2, 2025
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,...
Generalizability of FDA-Approved AI-Enabled Medical Devices for Clinical Use.
JAMA network open
Apr 1, 2025
IMPORTANCE: The primary objective of any newly developed medical device using artificial intelligence (AI) is to ensure its safe and effective use in broader clinical practice.
Geriatric and gerontological physiotherapy in focus: a cross-sectional survey of education, clinical practice, and service availability across world physiotherapy member nations.
BMC medical education
Apr 1, 2025
BACKGROUND: The ageing global population necessitates specialised geriatric/gerontological physiotherapy services (GPTS) to address age-related conditions. We explored the current state of geriatric/gerontological physiotherapy (GPT) academic program...
Adoption of Large Language Model AI Tools in Everyday Tasks: Multisite Cross-Sectional Qualitative Study of Chinese Hospital Administrators.
Journal of medical Internet research
Apr 1, 2025
BACKGROUND: Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, ...
Artificial intelligence (AI) in nursing administration: Challenges and opportunities.
PloS one
Apr 1, 2025
Artificial Intelligence (AI) is increasingly transforming nursing administration by enhancing operational efficiency and supporting data-driven decision-making. This study explores registered nurses perceptions of AI in Saudi Arabia, focusing on both...
Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women.
Scientific reports
Mar 31, 2025
The aim of this study was to establish the optimal prediction model by comparing the prediction effect of 6 kinds of prediction models containing biochemical indexes on the risk of osteoporosis in middle-aged and elderly women in Tibet. This study ad...
Online Health Information-Seeking in the Era of Large Language Models: Cross-Sectional Web-Based Survey Study.
Journal of medical Internet research
Mar 31, 2025
BACKGROUND: As large language model (LLM)-based chatbots such as ChatGPT (OpenAI) grow in popularity, it is essential to understand their role in delivering online health information compared to other resources. These chatbots often generate inaccura...
Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study.
JMIR aging
Mar 31, 2025
BACKGROUND: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essential for tailoring treatments to specific patient phenotypes.
Natural language processing for identifying major bleeding risk in hospitalised medical patients.
Computers in biology and medicine
Mar 30, 2025
BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare costs. This study aims to assess the incidence and risk factors for major bleeding in hospitalised m...
A cross-sectional study comparing machine learning and logistic regression techniques for predicting osteoporosis in a group at high risk of cardiovascular disease among old adults.
BMC geriatrics
Mar 29, 2025
BACKGROUND: Osteoporosis has become a significant public health concern that necessitates the application of appropriate techniques to calculate disease risk. Traditional methods, such as logistic regression,have been widely used to identify risk fac...
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