Machine Learning Technology in Biomedical Engineering

dc.contributor.editorYu, Hongqing
dc.contributor.editorAlzoubi, Alaa
dc.contributor.editorZhao, Yifan
dc.contributor.editorDu Hongbo
dc.date.accessioned2025-07-01T09:04:11Z
dc.date.available2025-07-01T09:04:11Z
dc.date.freetoread2025-07-01
dc.date.issued2024-04-29
dc.descriptionThe 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.
dc.description.abstract"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
dc.description.bookTitleMachine Learning Technology in Biomedical Engineering
dc.description.journalNameBioengineering
dc.identifier.citationYu 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 2024en_UK
dc.identifier.elementsID673711
dc.identifier.isbn3725808031
dc.identifier.isbn9783725808038
dc.identifier.issn2306-5354
dc.identifier.urihttps://doi.org/10.3390/books978-3-7258-0804-5
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/24060
dc.publisherMDPIen_UK
dc.publisher.urihttps://www.mdpi.com/journal/bioengineering/special_issues/ZG3ISDXD72
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectScienceen_UK
dc.subjectfeature selectionen_UK
dc.subjectfeature scoringen_UK
dc.subjectinformation theoryen_UK
dc.subjectentropyen_UK
dc.subjectmutual information (MI)en_UK
dc.subjectdimension reductionen_UK
dc.subjectlow-dimensional embeddingen_UK
dc.subjectreconstruction erroren_UK
dc.subjectprincipal component analysis (PCA)en_UK
dc.subjectclusteringen_UK
dc.subjectblockchainen_UK
dc.subjectfederated learningen_UK
dc.subjectpandemic prevention and controlen_UK
dc.subjectprivacy-preservingen_UK
dc.subjectsynthetic medical dataen_UK
dc.subjecttype 2 diabetesen_UK
dc.subjectprediction of diseasesen_UK
dc.subjectshufflingen_UK
dc.subjecthybrid deep neural networken_UK
dc.subjectfeature fusionen_UK
dc.subjectpathological gait recognitionen_UK
dc.subjectskeleton-based gait analysisen_UK
dc.subjectAI automationen_UK
dc.subjectbiomedicalen_UK
dc.subjectmachine learningen_UK
dc.subjectmicroservicesen_UK
dc.subjectknowledge graphen_UK
dc.subjectsemantic web services (SWS)en_UK
dc.subjectdiabetes mellitus (DM)en_UK
dc.subjectartificial intelligenceen_UK
dc.subjectfeature importanceen_UK
dc.subjectpredictive systemen_UK
dc.subjectglycosylated hemoglobin (HbA1c)en_UK
dc.subjectwell-controlled HbA1cen_UK
dc.subjectdiabetes-related diseaseen_UK
dc.subjectnutrition educationen_UK
dc.subjectphotoplethysmographyen_UK
dc.subjectHbA1cen_UK
dc.subjectblood glucoseen_UK
dc.subjectinduced potentialsen_UK
dc.subjectMRIen_UK
dc.subjecttime and frequency analysisen_UK
dc.subjectstationarity testen_UK
dc.subjectKPSS testen_UK
dc.subjectsurrogatesen_UK
dc.subjectbiomedical engineeringen_UK
dc.subjectimage and signal processingen_UK
dc.subjectmedical image analysis and medical decision-makingen_UK
dc.subjectcalibrationen_UK
dc.subjectdiabetic retinopathyen_UK
dc.subjectdistribution shiften_UK
dc.subjectfundus imageen_UK
dc.subjectrobustnessen_UK
dc.subjectknee cartilage osteoarthritis (KOA)en_UK
dc.subjectmagnetic resonance imaging (MRI) segmentationen_UK
dc.subjectmulti-atlasen_UK
dc.subjectgraph neural networks (GNNs)en_UK
dc.subjectdeep learningen_UK
dc.subjectgraph learningen_UK
dc.subjectsemi-supervised learning (SSL)en_UK
dc.titleMachine Learning Technology in Biomedical Engineeringen_UK
dc.typeBook

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