Yu, HongqingAlzoubi, AlaaZhao, YifanDu Hongbo2025-07-012025-07-012024-04-29Yu H, Alzoubi A, Zhao Y. (2024) Machine Learning Technology in Biomedical Engineering. Basel, MDPI. A special issue of Bioengineering. This special issue belongs to the section "Biomedical Engineering and Biomaterials". April 2024372580803197837258080382306-5354https://doi.org/10.3390/books978-3-7258-0804-5https://dspace.lib.cranfield.ac.uk/handle/1826/24060The 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."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 researchersAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Sciencefeature selectionfeature scoringinformation theoryentropymutual information (MI)dimension reductionlow-dimensional embeddingreconstruction errorprincipal component analysis (PCA)clusteringblockchainfederated learningpandemic prevention and controlprivacy-preservingsynthetic medical datatype 2 diabetesprediction of diseasesshufflinghybrid deep neural networkfeature fusionpathological gait recognitionskeleton-based gait analysisAI automationbiomedicalmachine learningmicroservicesknowledge graphsemantic web services (SWS)diabetes mellitus (DM)artificial intelligencefeature importancepredictive systemglycosylated hemoglobin (HbA1c)well-controlled HbA1cdiabetes-related diseasenutrition educationphotoplethysmographyHbA1cblood glucoseinduced potentialsMRItime and frequency analysisstationarity testKPSS testsurrogatesbiomedical engineeringimage and signal processingmedical image analysis and medical decision-makingcalibrationdiabetic retinopathydistribution shiftfundus imagerobustnessknee cartilage osteoarthritis (KOA)magnetic resonance imaging (MRI) segmentationmulti-atlasgraph neural networks (GNNs)deep learninggraph learningsemi-supervised learning (SSL)Book673711