AIMC Topic: Accidental Falls
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Privacy-Preserving Cameras for Fall Detection: Data Acquisition for Artificial Intelligence.
Computers, informatics, nursing : CIN
Jul 1, 2024
Enhancing fall risk assessment: instrumenting vision with deep learning during walks.
Journal of neuroengineering and rehabilitation
Jun 22, 2024
BACKGROUND: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking pro...
Falls Prevention Using AI and Remote Surveillance in Nursing Homes.
Journal of the American Medical Directors Association
Jun 13, 2024
The older population of United States is growing, with more adults having complicated medical conditions being admitted into nursing facilities and assisted living facilities. With the COVID-19 pandemic, the biggest challenge has been falls preventio...
Using machine learning algorithms to detect fear of falling in people with multiple sclerosis in standardized gait analysis.
Multiple sclerosis and related disorders
Jun 10, 2024
INTRODUCTION: Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. The progressive impairment of gait is one of the most important pathognomic symptoms which are associated with falls and fear of fall...
Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
May 21, 2024
PURPOSE: To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).
A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness.
Journal of biomedical informatics
May 11, 2024
OBJECTIVES: We evaluated methods for preparing electronic health record data to reduce bias before applying artificial intelligence (AI).
Accurate fall risk classification in elderly using one gait cycle data and machine learning.
Clinical biomechanics (Bristol, Avon)
May 8, 2024
BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. ...
The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review.
Journal of medical Internet research
Apr 29, 2024
BACKGROUND: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligen...
Fall prediction in a quiet standing balance test via machine learning: Is it possible?
PloS one
Apr 16, 2024
The elderly population is growing rapidly in the world and falls are becoming a big problem for society. Currently, clinical assessments of gait and posture include functional evaluations, objective, and subjective scales. They are considered the gol...
Using machine learning models to predict falls in hospitalised adults.
International journal of medical informatics
Mar 23, 2024
BACKGROUND: Identifying patients at high risk of falling is crucial in implementing effective fall prevention programs. While the integration of information systems is becoming more widespread in the healthcare industry, it poses a significant challe...
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