Computational Intelligence in Healthcare
The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models...
محفوظ في:
| التنسيق: | Online |
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| اللغة: | الإنجليزية |
| منشور في: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | ONIX_20220111_9783036523774_803 |
| الوسوم: |
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| _version_ | 1869521332154138624 |
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| collection | Directory of Open Access Books |
| description | The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications. |
| format | Online |
| id | doab-20.500.12854ir-76971 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-769712024-03-30T12:51:08Z Computational Intelligence in Healthcare Castellano, Giovanna Casalino, Gabriella sEMG deep learning neural networks gait phase classification everyday walking convolutional neural network CRISPR leukemia nucleus image segmentation soft covering rough set clustering machine learning algorithm soft computing multistage support vector machine model multiple imputation by chained equations SVM-based recursive feature elimination unipolar depression diabetic retinopathy (DR) pre-trained deep ConvNet uni-modal deep features multi-modal deep features transfer learning 1D pooling cross pooling IMU gait analysis long-term monitoring multi-unit multi-sensor time synchronization Internet of Medical Things body area network MIMU early detection sepsis evaluation metrics machine learning medical informatics feature extraction physionet challenge electrocardiogram Premature ventricular contraction sparse autoencoder unsupervised learning Softmax regression medical diagnosis artificial neural network e-health Tri-Fog Health System fault data elimination health status prediction health status detection health off diffusion tensor imaging ensemble learning decision support systems healthcare computational intelligence Alzheimer’s disease fuzzy inference systems genetic algorithms next-generation sequencing ovarian cancer interpretable models n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications. 2022-01-11T13:48:02Z 2022-01-11T13:48:02Z 2021 book ONIX_20220111_9783036523774_803 9783036523774 9783036523781 https://directory.doabooks.org/handle/20.500.12854/76971 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4563 https://mdpi.com/books/pdfview/book/4563 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-2378-1 10.3390/books978-3-0365-2378-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036523774 9783036523781 226 Basel, Switzerland open access |
| spellingShingle | sEMG deep learning neural networks gait phase classification everyday walking convolutional neural network CRISPR leukemia nucleus image segmentation soft covering rough set clustering machine learning algorithm soft computing multistage support vector machine model multiple imputation by chained equations SVM-based recursive feature elimination unipolar depression diabetic retinopathy (DR) pre-trained deep ConvNet uni-modal deep features multi-modal deep features transfer learning 1D pooling cross pooling IMU gait analysis long-term monitoring multi-unit multi-sensor time synchronization Internet of Medical Things body area network MIMU early detection sepsis evaluation metrics machine learning medical informatics feature extraction physionet challenge electrocardiogram Premature ventricular contraction sparse autoencoder unsupervised learning Softmax regression medical diagnosis artificial neural network e-health Tri-Fog Health System fault data elimination health status prediction health status detection health off diffusion tensor imaging ensemble learning decision support systems healthcare computational intelligence Alzheimer’s disease fuzzy inference systems genetic algorithms next-generation sequencing ovarian cancer interpretable models n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Computational Intelligence in Healthcare |
| title | Computational Intelligence in Healthcare |
| title_full | Computational Intelligence in Healthcare |
| title_fullStr | Computational Intelligence in Healthcare |
| title_full_unstemmed | Computational Intelligence in Healthcare |
| title_short | Computational Intelligence in Healthcare |
| title_sort | computational intelligence in healthcare |
| topic | sEMG deep learning neural networks gait phase classification everyday walking convolutional neural network CRISPR leukemia nucleus image segmentation soft covering rough set clustering machine learning algorithm soft computing multistage support vector machine model multiple imputation by chained equations SVM-based recursive feature elimination unipolar depression diabetic retinopathy (DR) pre-trained deep ConvNet uni-modal deep features multi-modal deep features transfer learning 1D pooling cross pooling IMU gait analysis long-term monitoring multi-unit multi-sensor time synchronization Internet of Medical Things body area network MIMU early detection sepsis evaluation metrics machine learning medical informatics feature extraction physionet challenge electrocardiogram Premature ventricular contraction sparse autoencoder unsupervised learning Softmax regression medical diagnosis artificial neural network e-health Tri-Fog Health System fault data elimination health status prediction health status detection health off diffusion tensor imaging ensemble learning decision support systems healthcare computational intelligence Alzheimer’s disease fuzzy inference systems genetic algorithms next-generation sequencing ovarian cancer interpretable models n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | sEMG deep learning neural networks gait phase classification everyday walking convolutional neural network CRISPR leukemia nucleus image segmentation soft covering rough set clustering machine learning algorithm soft computing multistage support vector machine model multiple imputation by chained equations SVM-based recursive feature elimination unipolar depression diabetic retinopathy (DR) pre-trained deep ConvNet uni-modal deep features multi-modal deep features transfer learning 1D pooling cross pooling IMU gait analysis long-term monitoring multi-unit multi-sensor time synchronization Internet of Medical Things body area network MIMU early detection sepsis evaluation metrics machine learning medical informatics feature extraction physionet challenge electrocardiogram Premature ventricular contraction sparse autoencoder unsupervised learning Softmax regression medical diagnosis artificial neural network e-health Tri-Fog Health System fault data elimination health status prediction health status detection health off diffusion tensor imaging ensemble learning decision support systems healthcare computational intelligence Alzheimer’s disease fuzzy inference systems genetic algorithms next-generation sequencing ovarian cancer interpretable models n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20220111_9783036523774_803 |