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Machine Learning Technology in Biomedical Engineering

"Machine Learning Technology in Biomedical Engineering" aims to provide a platform for researchers to showcase their latest research and findings on the application of machine learning technology in the field of biomedical engineering. The use of machine learning technology in healthcare has been gr...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2024
Matèries:
feature selection
feature scoring
information theory
entropy
mutual information (MI)
dimension reduction
low-dimensional embedding
reconstruction error
principal component analysis (PCA)
clustering
blockchain
federated learning
pandemic prevention and control
privacy-preserving
synthetic medical data
type 2 diabetes
prediction of diseases
shuffling
hybrid deep neural network
feature fusion
pathological gait recognition
skeleton-based gait analysis
AI automation
biomedical
machine learning
microservices
knowledge graph
semantic web services (SWS)
diabetes mellitus (DM)
artificial intelligence
feature importance
predictive system
glycosylated hemoglobin (HbA1c)
well-controlled HbA1c
diabetes-related disease
nutrition education
photoplethysmography
HbA1c
blood glucose
induced potentials
MRI
time and frequency analysis
stationarity test
KPSS test
surrogates
biomedical engineering
image and signal processing
medical image analysis and medical decision-making
calibration
diabetic retinopathy
distribution shift
fundus image
robustness
knee cartilage osteoarthritis (KOA)
magnetic resonance imaging (MRI) segmentation
multi-atlas
graph neural networks (GNNs)
deep learning
graph learning
semi-supervised learning (SSL)
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
Accés en línia:ONIX_20240514_9783725808038_533
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Sumari:"Machine Learning Technology in Biomedical Engineering" aims to provide a platform for researchers to showcase their latest research and findings on the application of machine learning technology in the field of biomedical engineering. The use of machine learning technology in healthcare has been growing rapidly in recent years and has the potential to revolutionize multiple aspects of healthcare, including disease diagnosis, treatment, and personalized medicine. This Special Issue covers a wide range of topics related to the application of machine learning in biomedical engineering, including predictive modelling, image and signal processing, deep learning, drug discovery, biomarker discovery, and medical decision making. By applying machine learning algorithms to large datasets of biomedical information, researchers and healthcare professionals can gain new insights into disease mechanisms, identify new biomarkers for disease, and develop more effective treatments. Machine learning algorithms can also be used to improve medical imaging analysis, automate medical diagnosis and decision making, and optimize drug-discovery processes. This Special Issue is significant because it encourages interdisciplinary collaboration between machine learning and biomedical-engineering researchers.

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