AIMC Topic: Adult

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Deep Collaborative Learning With Application to the Study of Multimodal Brain Development.

IEEE transactions on bio-medical engineering
OBJECTIVE: Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aim...

Analysis and evaluation of handwriting in patients with Parkinson's disease using kinematic, geometrical, and non-linear features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting a...

On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-Based Bio-Signal Decoding in BCI Speller Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain-computer interfaces (BCI) harnessing steady state visual evoked potentials (SSVEPs) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with al...

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophren...

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database.

PloS one
BACKGROUND: Intelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data fo...

Sleep staging from single-channel EEG with multi-scale feature and contextual information.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep staging methods can expedite sleep disorder diagnosis. The aim of this study was to propose a single-channel EEG sleep staging model, SleepStageNet, which e...

Use of artificial neural networks to identify the predictive factors of extracorporeal shock wave therapy treating patients with chronic plantar fasciitis.

Scientific reports
The purpose of our study is to identify the predictive factors for a minimum clinically successful therapy after extracorporeal shock wave therapy for chronic plantar fasciitis. The demographic and clinical characteristics were evaluated. The artific...

Measurement of Glomerular Filtration Rate using Quantitative SPECT/CT and Deep-learning-based Kidney Segmentation.

Scientific reports
Quantitative SPECT/CT is potentially useful for more accurate and reliable measurement of glomerular filtration rate (GFR) than conventional planar scintigraphy. However, manual drawing of a volume of interest (VOI) on renal parenchyma in CT images i...

A survey on what Australians with upper limb difference want in a prosthesis: justification for using soft robotics and additive manufacturing for customized prosthetic hands.

Disability and rehabilitation. Assistive technology
Upper limb prostheses are part of a rapidly changing market place. Despite development in device design, surveys report low levels of uptake and dissatisfaction with current prosthetic design. In this study, we present the results of a survey conduc...