AIMC Topic: Cross-Sectional Studies

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Factors associated with admission to elderly medical-welfare facilities in South Korea: a cross-sectional machine-learning study.

BMJ open
OBJECTIVES: To identify the key factors associated with admission to elderly medical-welfare facilities in South Korea and to evaluate their relative importance using machine learning techniques, providing an evidence base for policy in a rapidly age...

Development of the generative artificial intelligence awareness scale for secondary school students in Türkiye.

European journal of pediatrics
The widespread adoption of generative artificial intelligence (AI) in education and daily life necessitates a deeper understanding of students' awareness and attitudes. However, there is a lack of appropriate and psychometrically validated tools to a...

Distinct 3-Dimensional Morphologies of Arthritic Knee Anatomy Exist: CT-Based Phenotyping Offers Outlier Detection in Total Knee Arthroplasty.

The Journal of bone and joint surgery. American volume
BACKGROUND: There is no foundational classification that 3-dimensionally characterizes arthritic anatomy to preoperatively plan and postoperatively evaluate total knee arthroplasty (TKA). With the advent of computed tomography (CT) as a preoperative ...

Effects of attractions and social attributes on peoples' usage intention and media dependence towards chatbot: The mediating role of parasocial interaction and emotional support.

BMC psychology
PURPOSE: It is important to explore the relationship between humans and chatbots to improve human-robot interaction in the era of artificial intelligence. This study aims to explore the effects of attractions and social attributes of chatbots on user...

Using deep learning to predict internalizing problems from brain structure in youth.

Translational psychiatry
Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the bi...

Natural Language Processing and Coding for Detecting Bleeding Events in Discharge Summaries: Comparative Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: Bleeding adverse drug events (ADEs), particularly among older inpatients receiving antithrombotic therapy, represent a major safety concern in hospitals. These events are often underdetected by conventional rule-based systems relying on s...

A cross-sectional and bioinformatics-based analysis: perirenal fat thickness as a superior predictor of kidney stone disease.

Lipids in health and disease
BACKGROUND: Kidney stone disease (KSD) is a growing global health concern, with obesity (OB) as a major risk factor linked to metabolic dysfunction and chronic inflammation. Although the common method for evaluating OB is body mass index (BMI), it is...

Exploring the potential relationship between kidney disease index and cognitive dysfunction: a machine learning approach with NHANES data.

BMC geriatrics
OBJECTIVE: This study investigates the relationship between the Kidney Disease Index (KDI) and cognitive function, evaluating its potential as a predictive marker for cognitive impairment in older adults. We also compare the performance of KDI with t...

Prediction of Mini-Mental State Examination Scores for Cognitive Impairment and Machine Learning Analysis of Oral Health and Demographic Data Among Individuals Older Than 60 Years: Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: As the older population grows, so does the prevalence of cognitive impairment, emphasizing the importance of early diagnosis. The Mini-Mental State Examination (MMSE) is vital in identifying cognitive impairment. It is known that degraded...