AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population-Based Study.

JMIR cardio
BACKGROUND: Coronary heart disease (CHD) is a major cause of morbidity and mortality worldwide. Identifying key risk factors is essential for effective risk assessment and prevention. A data-driven approach using machine learning (ML) offers advanced...

All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning.

Clinical interventions in aging
INTRODUCTION: The aim of this study was to identify the influencing factors for all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models.

Reactions and Engagement of Individuals with Dementia Toward Humanoid Assistive Robots: A Study Using the Pepper Robot.

Studies in health technology and informatics
While robot acceptance in different populations is well-studied, little is known about how individuals with dementia perceive and respond to humanoid assistive robots. This paper explores how individuals affected by dementia react to and engage with ...

Development and validation of a novel predictive model for dementia risk in middle-aged and elderly depression individuals: a large and longitudinal machine learning cohort study.

Alzheimer's research & therapy
BACKGROUND: Depression serves as a prodromal symptom of dementia, and individuals with depression exhibit a significantly higher risk of developing dementia. The aim of this study is to develop and validate a novel dementia risk prediction tool among...

Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.

BMC emergency medicine
BACKGROUND: Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care upon arrival are crucial for improving survival. In Japan, helicopter emergency medical services (HEM...

Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study.

JMIR aging
BACKGROUND: The global cancer burden is rapidly increasing, with 20 million new cases estimated in 2022. The world population aged ≥65 years is also increasing, projected to reach 15.9% by 2050, making cancer control for older patients urgent. Surgic...

Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.

Alzheimer's research & therapy
BACKGROUND: Exploring the early stages of Alzheimer's disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for affected individuals and their families. However, the study of the early stages of AD involves anal...

Patients' Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) is increasingly used in medical care, particularly in the areas of image recognition and processing. While its practical use in other areas is still limited, an understanding of patients' needs is essential fo...

Artificial intelligence-guided distal radius fracture detection on plain radiographs in comparison with human raters.

Journal of orthopaedic surgery and research
BACKGROUND: The aim of this study was to compare the performance of artificial intelligence (AI) in detecting distal radius fractures (DRFs) on plain radiographs with the performance of human raters.

Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain.

Scientific reports
Pain is common in middle-aged and older adults, has also been identified as a fall risk factor, whereas the mechanism of falls in pain is unclear. This study included 13,074 middle-aged and older adults from the China health and retirement longitudin...