AIMC Topic: Aged, 80 and over

Clear Filters Showing 321 to 330 of 3331 articles

Attention-Guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI.

IEEE journal of biomedical and health informatics
Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic ...

Frailty Modeling Using Machine Learning Methodologies: A Systematic Review With Discussions on Outstanding Questions.

IEEE journal of biomedical and health informatics
Studying frailty is crucial for enhancing the health and quality of life among older adults, refining healthcare delivery methods, and tackling the obstacles linked to an aging demographic. Approaches to frailty modeling often utilise simple analytic...

Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.

Frontiers in public health
OBJECTIVE: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression.

A hierarchy category of socially assistive robots' functions: Insights from older adults.

Assistive technology : the official journal of RESNA
Socially assistive robots (SARs) are increasingly recognized for their potential in helping older adults age in place. Effectively meeting the diverse needs of older adults requires a proper classification of SARs' functions. However, existing functi...

Machine learning-based 28-day mortality prediction model for elderly neurocritically Ill patients.

Computer methods and programs in biomedicine
BACKGROUND: The growing population of elderly neurocritically ill patients highlights the need for effective prognosis prediction tools. This study aims to develop and validate machine learning (ML) models for predicting 28-day mortality in intensive...

An explainable predictive machine learning model of gangrenous cholecystitis based on clinical data: a retrospective single center study.

World journal of emergency surgery : WJES
BACKGROUND: Gangrenous cholecystitis (GC) is a serious clinical condition associated with high morbidity and mortality rates. Machine learning (ML) has significant potential in addressing the diverse characteristics of real data. We aim to develop an...

Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study.

BMC emergency medicine
BACKGROUND: In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The ri...

Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.

Lung cancer (Amsterdam, Netherlands)
BACKGROUND: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with ad...