AIMC Topic: Aged, 80 and over
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Establishing a Deep Learning Model That Integrates Pretreatment and Midtreatment Computed Tomography to Predict Treatment Response in Non-Small Cell Lung Cancer.
International journal of radiation oncology, biology, physics
Mar 13, 2025
PURPOSE: Patients with identical stages or similar tumor volumes can vary significantly in their responses to radiation therapy (RT) due to individual characteristics, making personalized RT for non-small cell lung cancer (NSCLC) challenging. This st...
Caregiving Artificial Intelligence Chatbot for Older Adults and Their Preferences, Well-Being, and Social Connectivity: Mixed-Method Study.
Journal of medical Internet research
Mar 13, 2025
BACKGROUND: The increasing number of older adults who are living alone poses challenges for maintaining their well-being, as they often need support with daily tasks, health care services, and social connections. However, advancements in artificial i...
Compliance Evaluation with ChatGPT for Diagnosis and Treatment in Patients Brought to the ED with a Preliminary Diagnosis of Stroke.
Prehospital emergency care
Mar 13, 2025
OBJECTIVES: Chat Generative Pre-trained Transformer (ChatGPT) is a natural language processing product developed by OpenAI. Recently, the use of ChatGPT has gained attention in the field of health care, particularly for its potential applications in ...
Association between muscle mass assessed by an artificial intelligence-based ultrasound imaging system and quality of life in patients with cancer-related malnutrition.
Nutrition (Burbank, Los Angeles County, Calif.)
Mar 12, 2025
INTRODUCTION: Emerging evidence suggests that diminished skeletal muscle mass is associated with lower health-related quality of life (HRQOL) in individuals with cancer. There are no studies that we know of in the literature that use ultrasound syste...
Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression.
Aging clinical and experimental research
Mar 12, 2025
BACKGROUND: Early prediction of progression in dementia is of major importance for providing patients with adequate clinical care, with considerable impact on the organization of the whole healthcare system.
Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography.
Scientific reports
Mar 12, 2025
Quantifying aortic valve calcification is critical for assessing the severity of aortic stenosis, predicting cardiovascular risk, and guiding treatment decisions. This study evaluated the feasibility of a deep learning-based automatic quantification ...
Development of a visualized risk prediction system for sarcopenia in older adults using machine learning: a cohort study based on CHARLS.
Frontiers in public health
Mar 12, 2025
INTRODUCTION: The older adult are at high risk of sarcopenia, making early identification and scientific intervention crucial for healthy aging.
Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool.
Annals of medicine
Mar 11, 2025
BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate preparation. This study develops and validates a machine learning model to predict bowel preparation adequa...
Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?
BMC medical informatics and decision making
Mar 11, 2025
OBJECTIVE: This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without osteoporosis. Various machine learning algorithms were employed for training and testing the model, ...
Develop and validate machine learning models to predict the risk of depressive symptoms in older adults with cognitive impairment.
BMC psychiatry
Mar 11, 2025
BACKGROUND: Cognitive impairment and depressive symptoms are prevalent and closely interrelated mental health issues in the elderly. Traditional methods for identifying depressive symptoms in this population often lack effectiveness. Machine learning...
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