Geriatrics

Deep Learning-Based Recurrent Delirium Prediction in Critically Ill Patients.

OBJECTIVES: To predict impending delirium in ICU patients using recurrent deep learning.

UDRN: Unified Dimensional Reduction Neural Network for feature selection and feature projection.

Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The two independent branches of DR are feature selection (FS) and feature projection (FP). FS focuses on selecting a critical subset of dimensions but risks destroying the data distribution (structure). On the other hand, FP combines all the input features into...

Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age.

OBJECTIVE: To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than ...

Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome.

To evaluate the performance of machine learning (ML) models and to compare it with logistic regression (LR) technique in predicting cognitive impairme...

Artificial Intelligence-Assisted Meta-Analysis of the Frequency of ACE I/D Polymorphisms in Centenarians and Other Long-Lived Individuals.

Current research on the angiotensin-converting-enzyme () gene has yielded controversial results on whether different polymorphisms are linked with hu...

Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review.

An integrative review investigating the incorporation of artificial intelligence (AI) and machine learning (ML) based decision support systems in ment...

Nurses' perception towards care robots and their work experience with socially assistive technology during COVID-19: A qualitative study.

This study aimed to explore nurses' perceptions towards care robots and their work experiences in caring for older adults who use socially assistive t...

Interpretable machine learning for dementia: A systematic review.

INTRODUCTION: Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact....

Using Explainable Artificial Intelligence to Predict Potentially Preventable Hospitalizations: A Population-Based Cohort Study in Denmark.

BACKGROUND: The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of...

Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI.

Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive i...

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey.

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare s...

Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes.

Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by 'contextual explanations' that let the pract...

Socially Assistive Devices in Healthcare-a Systematic Review of Empirical Evidence from an Ethical Perspective.

Socially assistive devices such as care robots or companions have been advocated as a promising tool in elderly care in Western healthcare systems. Et...

Industrializing AI/ML during the end-to-end drug discovery process.

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all s...

Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization.

: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (outpu...

Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions.

Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an int...

Image processing and supervised machine learning for retinal microglia characterization in senescence.

The process of senescence impairs the function of cells and can ultimately be a key factor in the development of disease. With an aging population, se...

BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN.

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the e...

PRA-Net: Part-and-Relation Attention Network for depression recognition from facial expression.

Artificial intelligence methods are widely applied to depression recognition and provide an objective solution. Many effective automated methods for d...

Do artificial neural networks love sex? How the combination of artificial neural networks with evolutionary algorithms may help to identify gender influence in rheumatic diseases.

Although medical research has been performed predominantly on men both in preclinical and clinical studies, continuous efforts have been made to overc...