AIMC Topic: Adult

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Physiological indices of challenge and threat: A data-driven investigation of autonomic nervous system reactivity during an active coping stressor task.

Psychophysiology
We utilized a data-driven, unsupervised machine learning approach to examine patterns of peripheral physiological responses during a motivated performance context across two large, independent data sets, each with multiple peripheral physiological me...

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

EBioMedicine
BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information.

Locomotion Mode Recognition With Robotic Transtibial Prosthesis in Inter-Session and Inter-Day Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis. In this study, we proposed an adaptive recognition strategy to against the time-varying features o...

Neural-network classification of cardiac disease from P cardiovascular magnetic resonance spectroscopy measures of creatine kinase energy metabolism.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive...

Early prediction of epileptic seizures using a long-term recurrent convolutional network.

Journal of neuroscience methods
BACKGROUND: A seizure prediction system can detect seizures prior to their occurrence and allow clinicians to provide timely treatment for patients with epilepsy. Research on seizure prediction has progressed from signal processing analyses to machin...

Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

BMJ open
OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.

Automation of the Timed-Up-and-Go Test Using a Conventional Video Camera.

IEEE journal of biomedical and health informatics
The Timed-Up-and-Go (TUG) test is a simple clinical tool commonly used to quickly assess the mobility of patients. Researchers have endeavored to automate the test using sensors or motion tracking systems to improve its accuracy and to extract more r...

A Heuristic Approach to Overcome Architectural Barriers Using a Robotic Wheelchair.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The Mobility Enhancement roBotic (MEBot) wheelchair was developed to improve the safety and accessibility of wheelchair users when facing architectural barriers. MEBot uses pneumatic actuators attached to its frame and six wheels to provide curb asce...

Initial dosing of intermittent vancomycin in adults: estimation of dosing interval in relation to dose and renal function.

European journal of hospital pharmacy : science and practice
OBJECTIVES: Due to the high interindividual variability in vancomycin pharmacokinetics, optimisation of its dosing is still challenging. This study aimed to explore vancomycin pharmacokinetics in adult patients and to propose an easy applicable dosin...

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI.

Magnetic resonance in medicine
PURPOSE: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted MRI (DW-MRI) data and evaluates its performance.