AIMC Topic: Geriatric Assessment
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Advances of artificial intelligence in predicting frailty using real-world data: A scoping review.
Ageing research reviews
Oct 5, 2024
BACKGROUND: Frailty assessment is imperative for tailoring healthcare interventions for older adults, but its implementation remains challenging due to the effort and time needed. The advances of artificial intelligence (AI) and natural language proc...
The application of machine learning for identifying frailty in older patients during hospital admission.
BMC medical informatics and decision making
Sep 27, 2024
BACKGROUND: Early identification of frail patients and early interventional treatment can minimize the frailty-related medical burden. This study investigated the use of machine learning (ML) to detect frailty in hospitalized older adults with acute ...
Predicting malnutrition-based anemia in geriatric patients using machine learning methods.
Journal of evaluation in clinical practice
Sep 23, 2024
BACKGROUND: Anemia due to malnutrition may develop as a result of iron, folate and vitamin B12 deficiencies. This situation poses a higher risk of morbidity and mortality in the geriatric population than in other age groups. Therefore, early diagnosi...
FRELSA: A dataset for frailty in elderly people originated from ELSA and evaluated through machine learning models.
International journal of medical informatics
Aug 19, 2024
BACKGROUND: Frailty is an age-related syndrome characterized by loss of strength and exhaustion and associated with multi-morbidity. Early detection and prediction of the appearance of frailty could help older people age better and prevent them from ...
Development and External Validation of a Machine Learning-based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study.
Journal of the American Medical Directors Association
Jul 24, 2024
OBJECTIVES: To develop and externally validate a machine learning-based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification...
Predictive model for assessing malnutrition in elderly hospitalized cancer patients: A machine learning approach.
Geriatric nursing (New York, N.Y.)
Jun 15, 2024
BACKGROUND: Malnutrition is prevalent among elderly cancer patients. This study aims to develop a predictive model for malnutrition in hospitalized elderly cancer patients.
Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours.
Clinical interventions in aging
Jun 12, 2024
BACKGROUND: The global aging population presents a significant challenge, with older adults experiencing declining physical and cognitive abilities and increased vulnerability to chronic diseases and adverse health outcomes. This study aims to develo...
Machine-learning classifier models for predicting sarcopenia in the elderly based on physical factors.
Geriatrics & gerontology international
May 14, 2024
AIM: As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X-rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose ...
Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index.
Frontiers in public health
May 9, 2024
BACKGROUND: Physical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteop...
Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification.
Frontiers in endocrinology
Apr 30, 2024
BACKGROUND: Prognostic risk stratification in older adults with type 2 diabetes (T2D) is important for guiding decisions concerning advance care planning.
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