Health 4.0 helps devise smart, cyber-physical systems via AI, nanorobots, and new ICT technologies.

What is it about?

https://www.routledge.com/Intelligent-Healthcare-Systems/Estrela/p/book/9781032052724 Cyber-physical systems, blockchain, health4.0, artificial intelligence, cloud computing, fog computing, healthcare, smart designs, robotics, nanorobotics, pathology, hematology, image processing, biomedical engineering, theragnostics, biometrics, deep learning, metaheuristics, MRI, deep learning, ad hoc networks, nanotechnology, digital twins, PET, wearables, wireless communications, public health, Epidemiology, Disease Mapping, HEVC, 3D image compression, volumetric theragnostics, drug development, drug delivery, in-body device, on-body device, internet Layers, CBIR, remote healthcare, brain-computer interface, BCI, in-body networks, on-body networks.

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Photo by National Cancer Institute on Unsplash

Photo by National Cancer Institute on Unsplash

Why is it important?

https://www.routledge.com/Intelligent-Healthcare-Systems/Estrela/p/book/9781032052724 Information is paramount to the healthcare sector, entailing intense data, medical epidemiologic sets, Internet browsing records, surveys, complex engineering models, and so on via the Cloud. This quest for knowledge prompts the data dimensionality, which calls for more sophisticated and efficient information strategies. Health science and biology are very complex fields fully embedded in information technology, but the associated processes are much too intricate to be faithfully modeled. It is not easy to extract knowledge starting from raw data, and it is also expensive. Artificial intelligence (AI) in healthcare (AIH) has been the primary concern to develop expert systems aimed for diagnostic and decision-making in knowledge acquisition, representation, reasoning, and explanation. Many healthcare facilities (HFs) have data acquisition, monitoring, and storage systems integrated into larger-scale information systems. This vast amount of information and databases stemming from medical applications cause hinder analysis and decision making. Hence, there is a need to develop better tools for accessing/storing/analyzing knowledge while effectively using multimodal data. These necessities become essential in the healthcare realm as decision-making relies on knowledge from multidisciplinary areas. This book intends to provide computational methods for intelligent health data analysis to narrow the gap between data gathering and data comprehension with applications in medicine, health care, biology, pharmacology, and related areas. Intelligent Data Analysis (IDA) expedites healthcare analyses and applications. IDA employs specialized statistical, pattern recognition, machine learning (ML), data abstraction, and visualization tools for analysis of data and discovery of mechanisms that created them. Healthcare data typically involve many records/variables, subtle interactions between entities, or a combination of all factors. Engineering, computing science, and ML empower data analysis tasks. The IDA extracts knowledge from too much data, with a vast amount of variables, data that represents very complex, nonlinear, real-life problems. IDA can help raw data analysis, coping with prediction tasks without knowing the theoretical description of the underlying process, classification tasks of new events, or modeling unknown processes. Classification, prediction, and modeling are the cornerstones brought in by IDA.

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