Handbook of Research on Artificial Intelligence and Soft Computing Techniques in Personalized Healthcare Services

Description

his volume demonstrates the diverse state-of-the-art applications that combine Artificial Intelligence with soft computing, which has great potential for creating smart personalized healthcare services. The book showcases the myriad uses of AI and computer techniques in healthcare that employ Deep Learning, robotics, Machine Learning, blockchain, emerging cloud, edge computing, Practical Byzantine Fault Tolerance consensus, CNN architecture, Splunk, genetic algorithms (GA), DurBhashan, and many more. These technologies can be used in healthcare for enhanced data sharing, remote health monitoring, tele-rehabilitation, connecting rural populations with healthcare services, identifying diseases and health issues, automated medical diagnosis, analyzing information in surgical videos, ensuring timely communication and transportation during health disasters and emergencies, for optimizing expenditures, and more. Soft computing approaches like particle swarm optimization (PSO), genetic algorithm (GA); ANN (artificial neural networks) is effectively used for diagnosing and prediction of disease from healthcare data of genetics, physiology, radiology, and cardiology and neurology disciplines. AI and soft computing can be used to make smart personalized healthcare services to build next generation ultra-digital hospitals with complete AI methodology, robotic surgery, 5G infrastructure for remote assistance, etc. The scope of this book covers not only background related to AI but also state-of-the-art applications in AI and soft computing to achieve intelligent personalized healthcare services. Chapter 1 covers how remote health monitoring systems can help patients get advice related to the required treatment from doctors. The first part of the module gives a brief idea and demonstration of systems with the help of an experimental setup using LabVIEW. The later part of the module covers how AI-IoMT can assist doctors in prescribing medicines in accordance with the patient’s critical parameters. This module also assesses numerous IoT security, privacy characteristics, various cyber threat models and attack classification. Chapter 2 discusses the use of huge biomedical things data with emerging cloud and edge computing technologies to identify diseases with high accuracy. Chapter 3 emphasizes the accurate identification and diagnosis of disease using Artificial Intelligence and Machine Learning in today’s unpredictable world environment. Chapter 4 presents an approach for accessing patient records using blockchain. This chapter deals with ways to enhance data-sharing application of the health sector and pharmacy that are built on concepts of hyper ledger fabric and practical Byzantine fault tolerance consensus. Chapter 5 discusses the various security issues and intelligent solutions for IoMT-enabled healthcare systems. This chapter covers a variety of security, privacy issues, and various attacks on smart devices, intrusion detection systems and intelligent solutions such as malware identification for safeguarding healthcare data. ... Chapter 18 proposes and analyzes an optimized reinforcement learning method in a disaster scenario and how this method is applied to reduce the loss of human life by ensuring timely communication and transportation among various units. Chapter 19 discusses the model for scheduling and allocation of healthcare resources as per patient’s physical activity needs. The model can be utilized to apply different scheduling algorithms and select the efficient ones to reduce waiting time of a patient.