AIMC Topic: Aged, 80 and over

Clear Filters Showing 1971 to 1980 of 3658 articles

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

PloS one
OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to pre...

Predicting conversion to wet age-related macular degeneration using deep learning.

Nature medicine
Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the ...

An automated machine learning approach to predict brain age from cortical anatomical measures.

Human brain mapping
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a m...

Predicting hospital admission for older emergency department patients: Insights from machine learning.

International journal of medical informatics
BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories of older adults seeking medical attention. Older adults present to the ED with distinct needs and com...

Identifying drugs with disease-modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology.

Pharmacoepidemiology and drug safety
PURPOSE: The aim of the study was to assess the feasibility of an approach combining computational methods and pharmacoepidemiology to identify potentially disease-modifying drugs in Parkinson's disease (PD).

Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study.

The Lancet. Digital health
BACKGROUND: CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic importance. We aim...

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

Journal of the American Heart Association
BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician-level accuracy in detecting (acute) cardiac abnorm...

Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.

PloS one
Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset of overt clinical complications. Here, we developed neural network models to predict hypertension, h...