Geriatrics

Radiomics for classification of bone mineral loss: A machine learning study.

PURPOSE: The purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches.

Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease.

Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities t...

Pilot and Feasibility Study on Elderly Support Services Using Communicative Robots and Monitoring Sensors Integrated With Cloud Robotics.

PURPOSE: This pilot before-after study investigated the possible effects of communicative robots, used with a sensing system supported by cloud roboti...

Shaping technologies for older adults with and without dementia: Reflections on ethics and preferences.

As a result of several years of European funding, progressive introduction of assistive technologies in our society has provided many researchers and ...

A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease.

BACKGROUND AND OBJECTIVES: Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods ...

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease.

Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent...

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.

Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain h...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Com...

DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment.

BACKGROUND: Recently developed methods of protein contact prediction, a crucially important step for protein structure prediction, depend heavily on d...

Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis.

Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases...

Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.

BACKGROUND: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and hea...

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks.

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing ...

The Digital Health Revolution and People with Disabilities: Perspective from the United States.

This article serves as the introduction to this special issue on Mobile Health and Mobile Rehabilitation for People with Disabilities. Social, technol...

pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. Thi...

Fuzzy risk prediction of roof fall and rib spalling: based on FFTA-DFCE and risk matrix methods.

The working conditions of underground mining are complex and variable, and roof fall and rib spalling are one of the main types of accidents that can ...

System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks.

The world population ageing is on the rise, which has led to an increase in the demand for medical care due to diseases and symptoms prevalent in heal...

Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals.

BACKGROUND: The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. ...

A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis.

Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzhei...

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice...

Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer's disease.

PURPOSE: Knowing the course of Alzheimer's disease is very important to prevent the deterioration of the disease, and accurate segmentation of sensiti...