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Care of terminally ill / Palliative care

Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.

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Showing 547-567 of 6,160 articles
Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning.

Pressure injuries represent a major concern in many nations. These wounds result from prolonged pres...

DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images.

This paper presents a new deep regression model, which we call DeepDistance, for cell detection in i...

Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest.

Biomass and yield are key variables for assessing the production and performance of agricultural sys...

Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results.

Controlling the six legs of an insect walking in an unpredictable environment is a challenging task,...

Position and Force Control of a Soft Pneumatic Actuator.

Recent advances in robotic systems have increased the need for various kinds of robots in many field...

End-to-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism.

Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients....

Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection.

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early p...

Quantitative Assessment of Motor Function for Patients with a Stroke by an End-Effector Upper Limb Rehabilitation Robot.

With the popularization of rehabilitation robots, it is necessary to develop quantitative motor func...

End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection.

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a criti...

Evaluation of haptic devices and end-users: Novel performance metrics in tele-robotic microsurgery.

BACKGROUND: Here, we present performance evaluation methodology that distinguishes the performance o...

Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control.

In the paper, exponential synchronization issue is considered for memristive neural networks (MNNs) ...

Review of Stereo Matching Algorithms Based on Deep Learning.

Stereo vision is a flourishing field, attracting the attention of many researchers. Recently, levera...

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Risk stratification of young patients with hypertension remains challenging. Generally, machine lear...

End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation.

Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for ...

An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis.

Automatic skin lesion analysis of dermoscopy images remains a challenging topic. In this paper, we p...

On the localness modeling for the self-attention based end-to-end speech synthesis.

Attention based end-to-end speech synthesis achieves better performance in both prosody and quality ...

Reconstruction of natural visual scenes from neural spikes with deep neural networks.

Neural coding is one of the central questions in systems neuroscience for understanding how the brai...

Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.

OBJECTIVE: To construct and internally validate prediction models to estimate the risk of long-term ...

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising.

OBJECTIVE: Non-invasive fetal electrocardiography has the potential to provide vital information for...

DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI.

BACKGROUND: Resting state fMRI has emerged as a popular neuroimaging method for automated recognitio...

Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN.

PURPOSE: Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Ra...

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