Computational Intelligence and Its Applications in Healthcare

Examines the latest techniques of artificial intelligence applied to data mining in a variety of real-world healthcare applications

Jitendra Kumar Verma (Edited by), Sudip Paul (Edited by), Prashant Johri (Edited by)

9780128206041, Elsevier Science

Paperback, published 29 July 2020

264 pages, Approx. 160 illustrations
23.4 x 19 x 1.8 cm, 0.54 kg

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare.

1. The impact of Internet of Things and data semantics on decision making for outpatient monitoring
2. Deep-learning approaches for health care: Patients in intensive care
3. Brain MRI image segmentation using nature-inspired Black Hole metaheuristic clustering approach
4. Blockchain for public health: Technology, applications, and a case study
5. Compression and multiplexing of medical images using optical image processing
6. Analysis of skin lesions using machine learning techniques
7. Computational intelligence using ontology—A case study on the knowledge representation in a clinical decision support system
8. Neural network-based abnormality detection for electrocardiogram time signals
9. Machine learning approaches for acetic acid test based uterine cervix image analysis
10. Convolutional neural network for biomedical applications
11. Alzheimer’s disease classification using deep learning
12. Diabetic retinopathy identification using autoML
13. Knowledge-based systems in medical applications
14. Convolution neural network-based feature learning model for EEG-based driver alert/drowsy state detection
15. Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification

Subject Areas: Biomedical engineering [MQW]