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...
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| Format: | Book Chapter |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| 001 | doab_20_500_12854_76971 | ||
| 005 | 20220111 | ||
| 020 | |a books978-3-0365-2378-1 | ||
| 020 | |a 9783036523774 | ||
| 020 | |a 9783036523781 | ||
| 024 | 7 | |a 10.3390/books978-3-0365-2378-1 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a KNTX |2 bicssc | |
| 100 | 1 | |a Castellano, Giovanna |4 edt | |
| 700 | 1 | |a Casalino, Gabriella |4 edt | |
| 700 | 1 | |a Castellano, Giovanna |4 oth | |
| 700 | 1 | |a Casalino, Gabriella |4 oth | |
| 245 | 1 | 0 | |a Computational Intelligence in Healthcare |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (226 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Information technology industries |2 bicssc | |
| 653 | |a sEMG | ||
| 653 | |a deep learning | ||
| 653 | |a neural networks | ||
| 653 | |a gait phase | ||
| 653 | |a classification | ||
| 653 | |a everyday walking | ||
| 653 | |a convolutional neural network | ||
| 653 | |a CRISPR | ||
| 653 | |a leukemia nucleus image | ||
| 653 | |a segmentation | ||
| 653 | |a soft covering rough set | ||
| 653 | |a clustering | ||
| 653 | |a machine learning algorithm | ||
| 653 | |a soft computing | ||
| 653 | |a multistage support vector machine model | ||
| 653 | |a multiple imputation by chained equations | ||
| 653 | |a SVM-based recursive feature elimination | ||
| 653 | |a unipolar depression | ||
| 653 | |a diabetic retinopathy (DR) | ||
| 653 | |a pre-trained deep ConvNet | ||
| 653 | |a uni-modal deep features | ||
| 653 | |a multi-modal deep features | ||
| 653 | |a transfer learning | ||
| 653 | |a 1D pooling | ||
| 653 | |a cross pooling | ||
| 653 | |a IMU | ||
| 653 | |a gait analysis | ||
| 653 | |a long-term monitoring | ||
| 653 | |a multi-unit | ||
| 653 | |a multi-sensor | ||
| 653 | |a time synchronization | ||
| 653 | |a Internet of Medical Things | ||
| 653 | |a body area network | ||
| 653 | |a MIMU | ||
| 653 | |a early detection | ||
| 653 | |a sepsis | ||
| 653 | |a evaluation metrics | ||
| 653 | |a machine learning | ||
| 653 | |a medical informatics | ||
| 653 | |a feature extraction | ||
| 653 | |a physionet challenge | ||
| 653 | |a electrocardiogram | ||
| 653 | |a Premature ventricular contraction | ||
| 653 | |a sparse autoencoder | ||
| 653 | |a unsupervised learning | ||
| 653 | |a Softmax regression | ||
| 653 | |a medical diagnosis | ||
| 653 | |a artificial neural network | ||
| 653 | |a e-health | ||
| 653 | |a Tri-Fog Health System | ||
| 653 | |a fault data elimination | ||
| 653 | |a health status prediction | ||
| 653 | |a health status detection | ||
| 653 | |a health off | ||
| 653 | |a diffusion tensor imaging | ||
| 653 | |a ensemble learning | ||
| 653 | |a decision support systems | ||
| 653 | |a healthcare | ||
| 653 | |a computational intelligence | ||
| 653 | |a Alzheimer's disease | ||
| 653 | |a fuzzy inference systems | ||
| 653 | |a genetic algorithms | ||
| 653 | |a next-generation sequencing | ||
| 653 | |a ovarian cancer | ||
| 653 | |a interpretable models | ||
| 653 | |a n/a | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/4563 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76971 |7 0 |z DOAB: description of the publication |