Semantic Web for Effective Healthcare Systems

Vishal Jain, Jyotir Moy Chatterjee, Ankita Bansal, Abha Jain, Vishal Jain, Jyotir Moy Chatterjee, Ankita Bansal, Abha Jain

• Publisher: Wiley-Scrivener
• Year: 2021
• Print ISBN: 9781119762294
• eBook ISBN: 9781119764151
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

SEMANTIC WEB FOR EFFECTIVE HEALTHCARE SYSTEMS

The book summarizes the trends and current research advances in web semantics, delineating the existing tools, techniques, methodologies, and research solutions

Semantic Web technologies have the opportunity to transform the way healthcare providers utilize technology to gain insights and knowledge from their data and make treatment decisions. Both Big Data and Semantic Web technologies can complement each other to address the challenges and add intelligence to healthcare management systems.

The aim of this book is to analyze the current status on how the semantic web is used to solve health data integration and interoperability problems, and how it provides advanced data linking capabilities that can improve search and retrieval of medical data. Chapters analyze the tools and approaches to semantic health data analysis and knowledge discovery. The book discusses the role of semantic technologies in extracting and transforming healthcare data before storing it in repositories. It also discusses different approaches for integrating heterogeneous healthcare data.

This innovative book offers:

• The first of its kind and highlights only the ontology driven information retrieval mechanisms and techniques being applied to healthcare as well as clinical information systems;
• Presents a comprehensive examination of the emerging research in areas of the semantic web;
• Discusses studies on new research areas including ontological engineering, semantic annotation and semantic sentiment analysis;
• Helps readers understand key concepts in semantic web applications for the biomedical engineering and healthcare fields;
• Includes coverage of key application areas of the semantic web.

Audience: Researchers and graduate students in computer science, biomedical engineering, electronic and software engineering, as well as industry scientific researchers, clinicians, and systems managers in biomedical fields.

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.

The report from British Columbia Safety and Quality Council says when patients and healthcare service entities are engaged in online platform, then there would be greater improvement in offering healthcare services. Improvement in healthcare services is visible when insights from the experience of patients are analyzed [5]. Hence, it becomes necessary to consolidate the opinions from the customers or clients so as to improve business, decision-making and increase revenue. Figure 1.1 gives the overview of decision-making process from the online product/service reviews, using different information extraction and text analysis techniques.

There exist many challenges while analyzing the social media text or user-generated content. In languages like English, the same word has multiple meaning (polysemy), and different words have same meaning (synonymy). People show “variety” and use heterogeneous words while expressing their views. It often leads to complication in processing the textual data. Most of the feature extraction techniques do not consider the semantic relationships between the terms. Subjectivity that exists in text processing techniques adds complexity to the process, which in turn impacts the evaluation of results. Also, the rare availability of gold-standard or annotated text data for different domains add more challenges to text analysis [6]. Hence, the identification and application of suitable Natural Language Processing (NLP) techniques are the main research focus in text data analysis.

Figure 1.1 Decision-making process from social media reviews.

Text analytics supports the context matching between the reader and the writer. This challenge can be managed if different vocabularies of features and their relationship are well represented in the data model. For example, content based contextual user feedback analysis enables the users to buy new products or avail any service by highlighting the best features of products or services. Challenges and issues in information retrieval problems are overcome if Ontology representation and topic modelling techniques a...

Table of contents

• Cover
• Table of Contents
• Title page
• Copyright
• Preface
• Acknowledgment
• 1 An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare
• 2 Semantic Web for Effective Healthcare Systems: Impact and Challenges
• 3 Ontology-Based System for Patient Monitoring
• 4 Semantic Web Solutions for Improvised Search in Healthcare Systems
• 5 Actionable Content Discovery for Healthcare
• 6 Intelligent Agent System Using Medicine Ontology
• 7 Ontology-Based System for Robotic Surgery—A Historical Analysis
• 8 IoT-Enabled Effective Healthcare Monitoring System Using Semantic Web
• 9 Precision Medicine in the Context of Ontology
• 10 A Knowledgebase Model Using RDF Knowledge Graph for Clinical Decision Support Systems
• 11 Medical Data Supervised Learning Ontologies for Accurate Data Analysis
• 12 Rare Disease Diagnosis as Information Retrieval Task
• 13 Atypical Point of View on Semantic Computing in Healthcare
• 14 Using Artificial Intelligence to Help COVID-19 Patients
• Index
• End User License Agreement