AIMC Topic: Accidental Falls
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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...
Correlation enhanced distribution adaptation for prediction of fall risk.
Scientific reports
Feb 12, 2024
With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older a...
A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women.
PloS one
Oct 31, 2023
Falls are a significant ongoing public health concern for older adults. At present, few studies have concurrently explored the influence of multiple measures when seeking to determine which variables are most predictive of fall risks. As such, this c...
Walking and falling: Using robot simulations to model the role of errors in infant walking.
Developmental science
Sep 26, 2023
What is the optimal penalty for errors in infant skill learning? Behavioral analyses indicate that errors are frequent but trivial as infants acquire foundational skills. In learning to walk, for example, falling is commonplace but appears to incur o...
Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.
Sensors (Basel, Switzerland)
Jul 18, 2023
Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data...
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