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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| Materialtyp: | Online |
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| Språk: | engelska |
| Utgiven: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Ämnen: | |
| Länkar: | ONIX_20240514_9783725808038_533 |
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| _version_ | 1869517993792241664 |
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| collection | Directory of Open Access Books |
| description | "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. |
| format | Online |
| id | doab-20.500.12854ir-137918 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1379182024-05-14T14:56:37Z Machine Learning Technology in Biomedical Engineering Yu, Hongqing AlZoubi, Alaa Zhao, Yifan Du, Hongbo 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 "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. 2024-05-14T14:56:32Z 2024-05-14T14:56:32Z 2024 book ONIX_20240514_9783725808038_533 9783725808038 9783725808045 https://directory.doabooks.org/handle/20.500.12854/137918 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9182 https://mdpi.com/books/pdfview/book/9182 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0804-5 10.3390/books978-3-7258-0804-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725808038 9783725808045 174 open access |
| spellingShingle | 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 Machine Learning Technology in Biomedical Engineering |
| title | Machine Learning Technology in Biomedical Engineering |
| title_full | Machine Learning Technology in Biomedical Engineering |
| title_fullStr | Machine Learning Technology in Biomedical Engineering |
| title_full_unstemmed | Machine Learning Technology in Biomedical Engineering |
| title_short | Machine Learning Technology in Biomedical Engineering |
| title_sort | machine learning technology in biomedical engineering |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20240514_9783725808038_533 |