Medical Data Processing and Analysis
The second edition of the Special Issue “Medical Data Processing and Analysis” highlights groundbreaking advancements in leveraging AI, ML, and emerging technologies for healthcare. Studies showcase innovative methods for enhancing diagnostic accuracy, such as explainable AI for autism detection, Pa...
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| Format: | Online |
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| Idioma: | anglès |
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MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Accés en línia: | ONIX_20250812T110751_9783725835034_46 |
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| _version_ | 1869525257405071360 |
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| collection | Directory of Open Access Books |
| description | The second edition of the Special Issue “Medical Data Processing and Analysis” highlights groundbreaking advancements in leveraging AI, ML, and emerging technologies for healthcare. Studies showcase innovative methods for enhancing diagnostic accuracy, such as explainable AI for autism detection, Pap smear analysis for cervical cancer, and retinal image segmentation for ocular conditions. Novel frameworks for medical image retrieval, disease detection using edge computing, and despeckling methods improve imaging precision and reliability. Hybrid models integrating blockchain, the IoT, and big data are proposed for monitoring infectious diseases, while assessments of interactive visualization tools such as BoldBI enhance trustworthiness in healthcare analytics. These contributions demonstrate how data-driven technologies can revolutionize early diagnosis, personalized treatment, and global health strategies. |
| format | Online |
| id | doab-20.500.12854ir-165290 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1652902025-08-12T09:18:02Z Medical Data Processing and Analysis Mustafa, Wan Azani Alquran, Hiam critical care machine learning acid–base balance prediction big data health informatics interactive visualization healthcare data data visualization trustworthiness assessment decision making driver stress recognition multimodal data deep learning CNN LSTM fuzzy EDAS metaheuristic method Bayesian optimization acquisition function VGGNet visual field defect NeuPD cell lines gene expression neural networks drug response prediction XGBoost Pap smear images AlexNet DarkNet-19 NasNet support vector machine random forest cervical cancer ant lion optimization particle swarm optimization speckle noise threshold nature-inspired minibatch water wave swarm optimization inveritible sparse fuzzy wavelet transform blockchain IoT devices AI algorithms big data analytics retinal image noise removal data imputation data augmentation GAN segmentation infectious diseases edge technology data assessment scheme disease detection risk assessment information analysis text-based medical image retrieval Convolutional Neural Network Medical-Dependent Features UMLS metathesaurus autism spectrum disorder clinical diagnosis data preprocessing healthcare analytics patient outcomes personalized intervention explainable artificial intelligence multi-modal machine learning COVID-19 diagnostics biomarkers and imaging SHAP and LIME analysis Parkinson’s disease handwriting convolutional neural networks dynamic analysis natural handwriting tasks n/a thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine The second edition of the Special Issue “Medical Data Processing and Analysis” highlights groundbreaking advancements in leveraging AI, ML, and emerging technologies for healthcare. Studies showcase innovative methods for enhancing diagnostic accuracy, such as explainable AI for autism detection, Pap smear analysis for cervical cancer, and retinal image segmentation for ocular conditions. Novel frameworks for medical image retrieval, disease detection using edge computing, and despeckling methods improve imaging precision and reliability. Hybrid models integrating blockchain, the IoT, and big data are proposed for monitoring infectious diseases, while assessments of interactive visualization tools such as BoldBI enhance trustworthiness in healthcare analytics. These contributions demonstrate how data-driven technologies can revolutionize early diagnosis, personalized treatment, and global health strategies. 2025-08-12T09:17:59Z 2025-08-12T09:17:59Z 2025 book ONIX_20250812T110751_9783725835034_46 9783725835034 9783725835041 https://directory.doabooks.org/handle/20.500.12854/165290 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10880 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3504-1 10.3390/books978-3-7258-3504-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725835034 9783725835041 314 open access |
| spellingShingle | critical care machine learning acid–base balance prediction big data health informatics interactive visualization healthcare data data visualization trustworthiness assessment decision making driver stress recognition multimodal data deep learning CNN LSTM fuzzy EDAS metaheuristic method Bayesian optimization acquisition function VGGNet visual field defect NeuPD cell lines gene expression neural networks drug response prediction XGBoost Pap smear images AlexNet DarkNet-19 NasNet support vector machine random forest cervical cancer ant lion optimization particle swarm optimization speckle noise threshold nature-inspired minibatch water wave swarm optimization inveritible sparse fuzzy wavelet transform blockchain IoT devices AI algorithms big data analytics retinal image noise removal data imputation data augmentation GAN segmentation infectious diseases edge technology data assessment scheme disease detection risk assessment information analysis text-based medical image retrieval Convolutional Neural Network Medical-Dependent Features UMLS metathesaurus autism spectrum disorder clinical diagnosis data preprocessing healthcare analytics patient outcomes personalized intervention explainable artificial intelligence multi-modal machine learning COVID-19 diagnostics biomarkers and imaging SHAP and LIME analysis Parkinson’s disease handwriting convolutional neural networks dynamic analysis natural handwriting tasks n/a thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine Medical Data Processing and Analysis |
| title | Medical Data Processing and Analysis |
| title_full | Medical Data Processing and Analysis |
| title_fullStr | Medical Data Processing and Analysis |
| title_full_unstemmed | Medical Data Processing and Analysis |
| title_short | Medical Data Processing and Analysis |
| title_sort | medical data processing and analysis |
| topic | critical care machine learning acid–base balance prediction big data health informatics interactive visualization healthcare data data visualization trustworthiness assessment decision making driver stress recognition multimodal data deep learning CNN LSTM fuzzy EDAS metaheuristic method Bayesian optimization acquisition function VGGNet visual field defect NeuPD cell lines gene expression neural networks drug response prediction XGBoost Pap smear images AlexNet DarkNet-19 NasNet support vector machine random forest cervical cancer ant lion optimization particle swarm optimization speckle noise threshold nature-inspired minibatch water wave swarm optimization inveritible sparse fuzzy wavelet transform blockchain IoT devices AI algorithms big data analytics retinal image noise removal data imputation data augmentation GAN segmentation infectious diseases edge technology data assessment scheme disease detection risk assessment information analysis text-based medical image retrieval Convolutional Neural Network Medical-Dependent Features UMLS metathesaurus autism spectrum disorder clinical diagnosis data preprocessing healthcare analytics patient outcomes personalized intervention explainable artificial intelligence multi-modal machine learning COVID-19 diagnostics biomarkers and imaging SHAP and LIME analysis Parkinson’s disease handwriting convolutional neural networks dynamic analysis natural handwriting tasks n/a thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine |
| topic_facet | critical care machine learning acid–base balance prediction big data health informatics interactive visualization healthcare data data visualization trustworthiness assessment decision making driver stress recognition multimodal data deep learning CNN LSTM fuzzy EDAS metaheuristic method Bayesian optimization acquisition function VGGNet visual field defect NeuPD cell lines gene expression neural networks drug response prediction XGBoost Pap smear images AlexNet DarkNet-19 NasNet support vector machine random forest cervical cancer ant lion optimization particle swarm optimization speckle noise threshold nature-inspired minibatch water wave swarm optimization inveritible sparse fuzzy wavelet transform blockchain IoT devices AI algorithms big data analytics retinal image noise removal data imputation data augmentation GAN segmentation infectious diseases edge technology data assessment scheme disease detection risk assessment information analysis text-based medical image retrieval Convolutional Neural Network Medical-Dependent Features UMLS metathesaurus autism spectrum disorder clinical diagnosis data preprocessing healthcare analytics patient outcomes personalized intervention explainable artificial intelligence multi-modal machine learning COVID-19 diagnostics biomarkers and imaging SHAP and LIME analysis Parkinson’s disease handwriting convolutional neural networks dynamic analysis natural handwriting tasks n/a thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine |
| url | ONIX_20250812T110751_9783725835034_46 |