Machine Learning Technology in Biomedical Engineering - (:unkn) unknown (MDPI, 2024)
"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
The Special Issue on "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 many aspects of healthcare, including disease diagnosis, treatment, and personalized medicine. The Special Issue will cover a wide range of topics related to the application of machine learning in biomedical engineering, including predictive modeling, image and signal processing, deep learning, drug discovery, biomarker discovery, and medical decision-making. Contributions from interdisciplinary teams combining expertise in machine learning and biomedical engineering are encouraged.
Keywords: Science; 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)