AIMC Topic: Accidental Falls
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Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain.
Scientific reports
May 16, 2025
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...
Machine Learning Scoring Reveals Increased Frequency of Falls Proximal to Death in Drosophila melanogaster.
The journals of gerontology. Series A, Biological sciences and medical sciences
May 5, 2025
Falls are a significant cause of human disability and death. Risk factors include normal aging, neurodegenerative disease, and sarcopenia. Drosophila melanogaster is a powerful model for study of normal aging and for modeling human neurodegenerative ...
Development of an Assistance Robot for Fall Detection and Reporting in Healthcare.
Studies in health technology and informatics
May 2, 2025
Falls pose a substantial risk to elderly individuals, especially those over 65, often leading to severe consequences. This project investigates the potential of the tēmi robot for fall detection in care facilities and its integration into a simulated...
Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.
European journal of neurology
May 1, 2025
BACKGROUND: Postural instability and gait difficulties are key symptoms of Parkinson's disease (PD), elevating the risk of falls substantially. Falls afflict 35% to 90% of PD patients, representing a major challenge in managing the condition. Accurat...
Automated Fall Detection in Smart Homes Using Multiple Radars and Machine Learning Classifiers.
Studies in health technology and informatics
Apr 8, 2025
Falls pose a significant risk, especially among elderly persons. Recently, radar sensors have been explored for fall detection. In this study, an attempt has been made to classify fall detection using multiple radars, machine learning (ML) classifier...
Automated identification of fall-related injuries in unstructured clinical notes.
American journal of epidemiology
Apr 8, 2025
Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses challenges for large-scale research. In this study, we developed and evaluated natural language process...
Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.
Journal of cachexia, sarcopenia and muscle
Feb 1, 2025
BACKGROUND: Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real-time location system (RTLS) enables continuous tracking of patient location, pro...
Application of machine learning for detecting high fall risk in middle-aged workers using video-based analysis of the first 3 steps.
Journal of occupational health
Jan 7, 2025
OBJECTIVES: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fa...
Urban-rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.
Frontiers in public health
Jan 1, 2025
BACKGROUND: Falls among older adults are a significant challenge to global healthy aging. Identifying key factors and differences in fall risks, along with developing predictive models, is essential for differentiated and precise interventions in Chi...
Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.
Studies in health technology and informatics
Jul 24, 2024
Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In thi...
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