AIMC Topic: Accidental Falls
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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...
The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.
Age and ageing
Jul 2, 2024
BACKGROUND: Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of the...
Effects of Language Differences on Inpatient Fall Detection Using Deep Learning.
Studies in health technology and informatics
Mar 1, 2024
This study examined the effects of language differences between Korean and English on the performance of natural language processing in the classification task of identifying inpatient falls from unstructured nursing notes.
AI-based decision support to optimize complex care for preventing medication-related falls.
Nature medicine
Mar 1, 2024
Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.
NeuroRehabilitation
Jan 1, 2024
BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at...
Better informing everyday fall risk assessment: experimental studies with contemporary technologies.
Lancet (London, England)
Nov 1, 2023
BACKGROUND: Age-related mobility issues and frailty are a major public health concern because of an increased risk of falls. Subjective assessment of fall risk in the clinic is limited, failing to account for an individual's habitual activities in th...
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