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Preface

The tremendous amount of data being generated on a daily basis in hospitals and other medical institutions needs to be properly harnessed and analyzed in order to gain useful insights. In other words, the health-related data needs to be explored in order to uncover valuable information that could lead to improved healthcare practices and the development of better biomedical products. However, there are many challenges, which need to be addressed before this goal is reached. One of the major challenges is interoperability of health and medical data. The data generated not only comes from different sources but also has inconsistencies in naming, structure, and format. An important requirement is to capture relevant data and also make it widely available for others to use. In addition to the data integration problem, user interaction with the data is another challenge. The difficulty lies in search handling, data navigation and data presentation. Finally, another challenge is how to use this huge amount of data to find valuable new patterns and transform such data into valuable knowledge, leading to potential improvement of resource utilization and patient health, and the development of biomedical products. There is a vast potential for data mining and data analytics tools in healthcare that could lead to useful information for decision making. In recent years, the Semantic Web has been gaining ground in addressing these challenges. The aim of this book is to analyze the current status on how the Semantic Web can be used to solve various challenges and enlighten readers with key advances in ontology-based information retrieval techniques in the healthcare domain. The following is a brief summary of the wide range of subjects covered throughout the book.

 - Chapter 1 discusses various information extraction techniques used to model the documents product/service reviews. The advantages of using the Semantic Web to ease communication between businesses and improve processes are also discussed.

 - Chapter 2 explores the impact of Semantic Web technologies and the challenges associated with their use in effective healthcare systems and also proposes solutions, which can be achieved with the present technology. In addition to this, some algorithms, frameworks, and real-time database systems realized with the help of artificial intelligence and web technology-based solutions are also discussed.

 - Chapter 3 focuses on the importance of an ontology-based system for a patient monitoring system. A domain ontology has been constructed to preserve the details of patient health issues. With the support of ontology, a patient monitoring system is constructed wherein data concerning every detail about the patient and their health is stored.

 - Chapter 4 highlights the role of Semantic Web technologies in improving services provided by healthcare systems. It elaborates on the search techniques used by researchers in the field to find the desired information. The role of semantics, how they are beneficial in the search process, and domain-specific resources are presented in detail. The latest technological advancements and resources from the bio-medical field are also discussed.

 - Chapter 5 discusses what actionable content should look like in practice and how it can become more efficient by aiding in clinical decision-making and administrative capability. The chapter renders various definitions of actionable content, and also focuses on the stages of health analytics and how ontology can be used for prescriptive health analytics.

 - Chapter 6 depicts the retrieval of ontology-based information from the medical literature database MEDLINE. The main focus of the chapter is to enhance the retrieval of information from the medical literature database and conduct the search with more clarity. The approach discussed to achieve this is the preliminary design and execution of an ontology-based intelligent agent system that applies Semantic Web language, which benefits efficient systematic retrieval of medical information.

 - Chapter 7 presents a historical analysis of an ontology-based system for robotic surgery and documents the most significant interventions of robots in medical surgery. The chapter discusses how the academic field has embraced this new discipline and how inclusive research on a worldwide scale has honed the design and method of robotic procedures, all while maintaining an impeccable metric.

 - Chapter 8 presents the applications of IoT in healthcare and how these applications can be used with the help of various sensors. It discusses the established strategies used by IoT-based devices to deal with patients, doctors, and hospitals in order to provide smarter and faster services. The authors propose an IoT-based architecture for monitoring the health of patients remotely.

 - Chapter 9 discusses the use of precision medicine in the context of ontology. It explains ontologies and their application in computational reasoning to promote an accurate classification of patients’ diagnoses and managing care, and for translational research.

 - Chapter 10 discusses the use of knowledge graphs for knowledge representation. A model for such a knowledgebase is proposed that makes use of open information extraction systems to capture relevant knowledge from medical literature and curate it in the knowledgebase of the clinical decision support system.

 - Chapter 11 covers all aspects related to the successful customization of data semantics, ontologies, clinical jobs, and free learning, and depicts the Unified Medical Language System (UMLS) framework used inside AQ21 rule learning programming. Ontologies are the quality systems for expressive genuine variables in clinical and flourishing fields.

 - Chapter 12 provides information on rare diseases and explores the relationship between rare diseases, diagnoses, and information retrieval. In particular, it illustrates the history, characteristics, types, and classification along with databases of rare disease information. It also explores the challenges faced by researchers in rare disease information retrieval and how they can be resolved by search query optimization.

 - Chapter 13 reviews the recent advances in medical terminology tools and application strategies currently in use for semantic reasoning and interoperability in healthcare. Common terminology standards used in health information and technology, such as SNOMED CT, RxNorm, LOINC, ICD-x-CM, and UCUM, are discussed. Also discussed are the current reference terminology mapping solutions that enable semantic interoperability of data between health systems.

 - Chapter 14 builds upon the existing AI-based model in order to discover a new model to improve healthcare facilities for the faster recovery of COVID-19 patients. The chapter discusses different AI-related solutions for the healthcare industry.

In conclusion, we are grateful to all those who directly and indirectly contributed to this book. We are also grateful to the publisher for giving us the opportunity to publish it.

Vishal Jain Jyotir Moy Chatterjee Ankita Bansal Abha Jain September 2021