Geriatrics

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit.

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonar...

Design of a 6-DoF Cost-effective Differential-drive based Robotic system for Upper-Limb Stroke Rehabilitation.

This paper discusses the design, construction, and characteristics of a six degree of freedom (6-DoF) robotic upper limb stroke rehabilitation device. The device is primarily designed to be used by stroke survivors suffering from hemi-paresis, post stroke therapy. The proposed device is aimed to substitute upper limb exoskeletons or end-effector robotic arm based rehabilitation systems, which are ...

End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder.

Rapid eye movement (REM) sleep behavior disorder (RBD) is parasomnia and a prodromal manifestation of Parkinson's disease. The current diagnostic meth...

Probing the link between the APOE-ε4 allele and whole-brain gray matter using deep learning.

The APOE-ε4 allele is a known genetic risk for Alzheimer's disease (AD). Thus, it can be reasoned that the APOE-ε4 allele would also impact neurodegen...

Designing a Social Robot Companion to Support Homecare: Usability Results.

Earlier studies show frail seniors often experience loneliness and depression. Moreover, frailty can lead problems with medication and nutrition patte...

Use of Robots to Support Those Living with Dementia and Their Caregivers.

Dementia and other related diseases causing symptoms of mild cognitive impairment are being increasingly diagnosed. These diseases are placing a signi...

Accuracies of Training Labels and Machine Learning Models: Experiments on Delirium and Simulated Data.

Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data is not always available, and imperfectly l...

[Technical difficulties and countermeasures of digestive tract reconstruction in robotic radical gastrectomy for gastric cancer].

There still remain some problemsin digestive tract reconstruction after robotic radical gastrectomy for gastric cancer at present, such as great surgi...

Explainable Artificial Intelligence in Ambulatory Digital Dementia Screenings.

Recently, digital apps have entered the market to enable the early diagnosis of dementia by offering digital dementia screenings. Some of these apps u...

The Prediction of Fall Circumstances Among Patients in Clinical Care - A Retrospective Observational Study.

Standardized fall risk scores have not proven to reliably predict falls in clinical settings. Machine Learning offers the potential to increase the ac...

GraphVAMPNet, using graph neural networks and variational approach to Markov processes for dynamical modeling of biomolecules.

Finding a low dimensional representation of data from long-timescale trajectories of biomolecular processes, such as protein folding or ligand-recepto...

Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation.

SIGNIFICANCE: To achieve early detection of osteoporosis, a simple bone densitometry method using optics was proposed. However, individual differences...

GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models.

Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminat...

[A spatial localization model of mobile robot based on entorhinal-hippocampal cognitive mechanism in rat brain].

Physiological studies reveal that rats rely on multiple spatial cells for spatial navigation and memory. In this paper, we investigated the firing mec...

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in ...

Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.

BACKGROUND: Delirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the oc...

[Structural design and performance analysis of an auxiliary dining robot].

An auxiliary dining robot is designed in this paper, which implements the humanoid feeding function with theory of inventive problem solving (TRIZ) th...

DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications.

SUMMARY: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. U...

Differential Role for Hippocampal Subfields in Alzheimer's Disease Progression Revealed with Deep Learning.

Mild cognitive impairment (MCI) is often considered the precursor of Alzheimer's disease. However, MCI is associated with substantially variable progr...

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.

Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) p...