AIMC Topic: Cross-Sectional Studies
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Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey.
BMC geriatrics
Nov 14, 2024
BACKGROUND: Loneliness is prevalent among the elderly and has intensified due to global aging trends. It adversely affects both mental and physical health. Traditional scales for measuring loneliness may yield biased results due to varying definition...
Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study.
Journal of medical Internet research
Nov 14, 2024
BACKGROUND: Artificial intelligence (AI) chatbots such as ChatGPT are expected to impact vision health care significantly. Their potential to optimize the consultation process and diagnostic capabilities across range of ophthalmic subspecialties have...
Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study.
Sensors (Basel, Switzerland)
Nov 13, 2024
BACKGROUND: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and...
Explaining deep learning models for age-related gait classification based on acceleration time series.
Computers in biology and medicine
Nov 12, 2024
BACKGROUND: Gait analysis holds significant importance in monitoring daily health, particularly among older adults. Advancements in sensor technology enable the capture of movement in real-life environments and generate big data. Machine learning, no...
Using advanced machine learning algorithms to predict academic major completion: A cross-sectional study.
Computers in biology and medicine
Nov 12, 2024
BACKGROUND: Existing prediction methods for academic majors based on personality traits have notable gaps, including limited model complexity and generalizability.The current study aimed to utilize advanced Machine Learning (ML) algorithms with smoot...
Representation of intensivists' race/ethnicity, sex, and age by artificial intelligence: a cross-sectional study of two text-to-image models.
Critical care (London, England)
Nov 11, 2024
BACKGROUND: Integrating artificial intelligence (AI) into intensive care practices can enhance patient care by providing real-time predictions and aiding clinical decisions. However, biases in AI models can undermine diversity, equity, and inclusion ...
Precision of artificial intelligence in paediatric cardiology multimodal image interpretation.
Cardiology in the young
Nov 11, 2024
Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the proficiency of artificial intelligence chatbots, like ChatGPT-4, in interpreting these images has not been assessed. This cross-sectional study evaluates...
Machine learning adjusted sequential CUSUM-analyses are superior to cross-sectional analysis of excess mortality after surgery.
International journal of medical informatics
Nov 9, 2024
BACKGROUND: The assessment of clinical outcome quality, particularly in surgery, is crucial for healthcare improvement. Traditional cross-sectional analyses often fall short in timely and systematic identification of clinical quality issues. This stu...
Height prediction of individuals with osteogenesis imperfecta by machine learning.
Orphanet journal of rare diseases
Nov 9, 2024
BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of...
Predicting frailty in older patients with chronic pain using explainable machine learning: A cross-sectional study.
Geriatric nursing (New York, N.Y.)
Nov 8, 2024
Frailty is common among older adults with chronic pain, and early identification is crucial in preventing adverse outcomes like falls, disability, and dementia. However, effective tools for identifying frailty in this population remain limited. This ...
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