Semantic Web for Effective Healthcare Systems. Группа авторов
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Название: Semantic Web for Effective Healthcare Systems
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: Программы
isbn: 9781119764151
isbn:
- 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
Acknowledgment
I would like to acknowledge the most important people in my life—my late grandfather Shri Gopal Chatterjee, my late grandmother Smt. Subhankori Chatterjee, my late mother Nomita Chatterjee, my uncle Shri. Moni Moy Chatterjee, and my father Shri. Aloke Moy Chatterjee. The book has been my long-cherished dream, which would not have become a reality without the support and love of these amazing people. They continued to encourage me despite my failing to give them the proper time and attention. I am also grateful to my friends, who have encouraged and blessed this work with their unconditional love and patience.
Jyotir Moy Chatterjee Department of IT Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation) Kathmandu, Nepal
1
An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare
A. M. Abirami1* and A. Askarunisa2
1Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
2Department of Computer Science and Engineering, KLN College of Information Technology, Madurai, Tamil Nadu, India
Abstract
The internet world contains large volume of text data. The integration of web sources is required to derive needed information. Human annotation is much difficult and tedious. Automated processing is necessary to make these data readable by machines. But mostly they are available in unstructured format, and they need to be formatted into structured form. Structured information is retrieved from unstructured or semi-structured text which is defined as text analytics. There are many Information Extraction (IE) techniques available to model the documents (product/service reviews). Vector space model uses only the content but not the contextual representation. This complexity is resolved by Semantic web, the initiative of WWW Consortium. The advantage of the use of Semantic web enables the ease of communication between Businesses and in process improvement.
Keywords: Ontology, semantic-web, decision making, healthcare, service, reviews
1.1 Introduction
Text analysis is defined as deriving structured data from unstructured text. Additional information like customer insight about the product or service can be retrieved from the unstructured data sources using text analytics techniques. Its techniques have different applications such as insurance claims assessment, competitor analysis, sentiment analysis and the like. Many industries use text analytics for their business improvement. Social media impacts different industries like product business [1, 2], tourism [3, 4], and healthcare service [5] with the tremendous changes in the recent past years.
Retrieving and summarizing web data, which are dispersed in different web pages, are difficult and complex processes; also, they consume most of the manual effort and time. No standard data model exists for web documents. This increases the necessity of annotating the huge number of text documents that exist in the World Wide Web (WWW). Extracting and collating the information from these text is a complex task. Unlike numerical dataset, text documents contain more number of features. The amount of resources required to represent big dataset may be improved by representing the text documents with most needed and non-redundant features. Classification or clustering algorithms may be used for identifying the features from the text documents. The documents are analyzed, modeled and then used in the process of business improvement or for personal interest. Thus, the annotated text improves automated decision-making process, which in turn reduces the manual effort and time required for text analysis.
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