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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Bibliographic Details
Other Authors:	Castellano, Giovanna (Editor), Casalino, Gabriella (Editor)
Format:	Book Chapter
Published:	Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:	Information technology industries 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
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Computational Intelligence in Healthcare

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