AIMC Topic: Accidental Falls
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique.
Sensors (Basel, Switzerland)
Feb 27, 2025
Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they...
Machine learning using random forest to differentiate between blow and fall situations of head trauma.
International journal of legal medicine
Feb 22, 2025
Blunt head trauma is a common occurrence in forensic practice. Interpreting the origin of craniocerebral injuries can be a challenging process, particularly when it comes to distinguishing between falls or inflicted blows. The objective of this study...
Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.
PloS one
Feb 3, 2025
BACKGROUND: Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a sign...
Constructing a fall risk prediction model for hospitalized patients using machine learning.
BMC public health
Jan 20, 2025
STUDY OBJECTIVES: This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.
Predicting fall parameters from infant skull fractures using machine learning.
Biomechanics and modeling in mechanobiology
Jan 18, 2025
When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always r...
Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.
Geriatrics & gerontology international
Jan 12, 2025
AIM: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a "signature" (combination of clinical variables) that could pred...
Interpretable machine learning for identifying overweight and obesity risk factors of older adults in China.
Geriatric nursing (New York, N.Y.)
Jan 4, 2025
OBJECTIVE: To estimate the importance of risk factors on overweight/obesity among older adults by comparing different predictive model.
Machine Learning Prediction for Postdischarge Falls in Older Adults: A Multicenter Prospective Study.
Journal of the American Medical Directors Association
Dec 30, 2024
OBJECTIVES: The study aimed to develop a machine learning (ML) model to predict early postdischarge falls in older adults using data that are easy to collect in acute care hospitals. This may reduce the burden imposed by complex measures on patients ...
Hope for the best prepare for the worst: acute kidney disease and catastrophic comorbidities (a case report).
The Pan African medical journal
Dec 27, 2024
It is evident that Acute Kidney Injury (AKI) is an independent risk factor for both the survival of patients and their kidneys. Here, we present a case of oliguric AKI secondary to blunt trauma-induced crush syndrome complicated with severe sepsis in...
Use of Hearing Aids Embedded with Inertial Sensors and Artificial Intelligence to Identify Patients at Risk for Falling.
Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
Dec 17, 2024
OBJECTIVE: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.
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