A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data

Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessmen...

Full description

Bibliographic Details
Main Authors: Karlen, W. (Author), Schwab, P. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
adult
Article
Artificial neural networks
attentive aggregation model
balance test
biological marker
Biomarkers
Central nervous systems
Clinical assessments
clinical trial
cohort analysis
controlled study
deep learning
Deep learning
Deep Learning
Diagnosis
diagnostic test accuracy study
digital biomarker
digital biomarkers
drawing test
explainability
fatigue
Fatigue
female
Free livings
human
Humans
hyperparameter
learning algorithm
Learning approach
Learning models
machine learning
major clinical study
male
medical diagnosis
middle aged
mobile application
mobility test
Monitoring
Multilayer neural networks
multilayer perceptron
multiple sclerosis
Multiple sclerosis
Multiple Sclerosis
neural attention
Neural Networks, Computer
observational study
pinching test
predictive performance
questionnaire
receiver operating characteristic
Receiver operating characteristic curves
sensitivity and specificity
smartphone
Smartphone
Smartphones
symbol matching
U turn test
unclassified drug
validation process
Visual functions
walking
Online Access:View Fulltext in Publisher

Similar Items

Similar Items