Handbook on Intelligent Healthcare Analytics

Knowledge Engineering with Big Data

A. Jaya, K. Kalaiselvi, Dinesh Goyal, Dhiya Al-Jumeily

• Publisher: Wiley-Scrivener
• Year: 2022
• Print ISBN: 9781119791799
• eBook ISBN: 9781119792536
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS

The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners.

The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.

A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare.

In addition, the reader will find in this Handbook:

• Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning;
• An exploration of predictive analytics in healthcare;
• The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics.

Audience
Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

1
An Introduction to Knowledge Engineering and Data Analytics

D. Karthika* and K. Kalaiselvi^†

Department of Computer Applications, Vels Institute of Science, Technology & Advanced Studies (Formerly Vels University), Chennai, Tamil Nadu, India

Abstract

In recent years, the philosophy of Knowledge Engineering has become important. Information engineering is an area of system engineering which meets unclear process demands by emphasizing the development of knowledge in a knowledge-based system and its representation. A broad architecture for knowledge engineering that manages the fragmented modeling and online learning of knowledge from numerous sources of information, non-linear incorporation of fragmented knowledge, and automatic demand-based knowledge navigation. The project aims to provide petabytes in the defined application domains with data and information tools. Knowledge-based engineering (KBE) frameworks are based on the working standards and core features with a special focus on their built-in programming language. This language is the key element of a KBE framework and promotes the development and re-use of the design skills necessary to model complex engineering goods. This facility allows for the automation of the process preparation step of multidisciplinary research (MDA), which is particularly important for this novel. The key types of design rules to be implemented in the implementation of the KBE are listed, and several examples illustrating the significant differences between the KBE and the traditional CAD approaches are presented. This chapter discusses KBE principles and how this technology will facilitate and enable the multidisciplinary optimization (MDO) of the design of complex products. This chapter discusses their reach goes beyond existing CAD structure constraints and other practical parametric and space exploration approaches. There is a discussion of the concept of KBE and its usage in architecture that supports the use of MDO. Finally, this chapter discusses on the key measures and latest trends in the development of KBE.

Keywords: Data analytics, knowledge, knowledge engineering, principles, knowledge acquisition

1.1 Introduction

1.1.1 Online Learning and Fragmented Learning Modeling

Applied artificial intelligence (AI) was defined as knowledge engineering [1], with three major scientific questions: knowledge representation, the use of information, and the acquisition of knowledge. In the big data age, the three fundamental problems must evolve with the basic characteristics of the complex and evolving connections between data objects, which are autonomous sources of information. Big data not only rely on domain awareness but also distribute knowledge from numerous information sources. To have knowledge of engineering tools for big data, we need tons of experience. Three primary research issues need to be addressed for the 54-month, RMB 45-million, 15-year Big Data Knowledge Engineering (BigKE) project sponsored by China’s Ministry of Science and Technology and several other domestic agencies: 1) online learning and fragmented learning modeling; 2) nonlinear fusion of fragmented information; and 3) multimedia fusion of knowledge. Discussing these topics is the main contribution of this article. With 1), we examine broken information and representation clusters, immersive online content learning with fragmented knowledge, and simulation with spatial and temporal characteristics of evolving knowledge. Question 2) will discuss connections, a modern pattern study, and dynamic integration between skills subsections of fragmented information. The key issues mentioned in Figure 1.1 will be collaborative, context-based computing, information browsing, route discovery, and the enhancement of interactive knowledge adaptation.

Figure 1.1 Knowledge engineering.

Due to these features of several channels, traditional offline data mining methods cannot stream data since it is important to reform the data. Online learning methods help solve this issue and adapt easily to the drift of the effects of streaming. But typical methods to online learning are explicitly configured for single-source info. Thus, the maintenance of these features concurrently provides great difficulties and opportunities for large-scale data production. Big data starts with global details, tackles dispersed data like data sources and function streams, and integrates diverse understanding from multiple data channels, as well as domain experience in personalized demand-driven knowledge services. In the age of big data, many data sources are usually heterogeneous and independent and require evolving, complex connections among data objects. These qualities are considered by substantial experience. Meanwhile, major suppliers of information provide personalized and in-house demand-driven offerings through the usage of large-scale information technologies [2].

Centered on the characteristics of multiple data sets, the key to a multisource retrieval of information is fragmented data processing [3]. To create global awareness, local information pieces from individual data points can be merged. Present online learning algorithms often use linear fitting for the retrieval of dispersed knowledge from local data sources [4]. In the case of fragmented knowledge fusion, though, linear fitting is not successful and may even create problems of overfitting. Several studies are ongoing to improve coherence in the processing and interpretation of fragmented knowledge [6], and the advantage of machine learning for large data interpreting is that most samples are efficient, thus eliminating the possibility of over-adjustment at any rate [7]. Big data innovation acquires knowledge mostly from user-produced content, as opposed to traditional information engineering’s focused on domain experience, in addition to authoritative sources of knowledge, such as technical knowledge bases. The content created by users provides a new type of database that could be used as a main human information provider as well as to help solve the problem of bottlenecks in traditional knowledge engineering. The information created by the consumer is broad and heterogeneous which leads to storage and indexing complexities [5], and the knowledge base should be able to build and disseminate itself to establish realistic models of data relations. For instance, for a range of reasons, clinical findings in survey samples can be incomplete and unreliable, and preprocessing is needed to improve analytical data [8].

Both skills are essential for the creation of personalized knowledge base tools as the knowledge base should be targeted to the needs of individual users. Huge information reinforces distributed expertise to develop any ability. Big data architecture also requires a customer interface to overcome user-specific problems. With the advent of science and innovations, in the fast-changing knowledge world of today, the nature of global economic growth has changed by introducing more communication models, shorter product life cycles, and a modern product production rate. Knowledge engineering is an AI field that implements systems based on information. Such structures provide computer applications with a broad variety of knowledge, policy, and reasoning mechanisms that provide answers to real-world issues. Difficulties dominated the early years of computer technology. Also, knowledge engineers find that it is a very long and expensive undertaking to obtain appropriate quality knowledge to construct a reliable and usable system. The construction of an expert system was identified as a knowledge learning bottleneck. This helped to gain skills and has been a big area of research in the field of information technology.

The purpose of gathering information is to create strategies and tools that make it as simple and effective as possible to gather and verify a professional’s expertise. Experts tend to be critical and busy individuals, and the techniques followed would also minimize the time expended on knowledge collection sessions by each expert. The key form of the knowledge-based approach is an expert procedure, which is intended to mimic an expert’s thinking processes. Typical examples of specialist systems include bacterial disease control, mining advice, and electronic circuit design assessment. It currently refers to the planning, administration, and construction of a system centered on expertise. It operates in a broad variety of aspects of computer technology, including data baselines, data collection, advanced networks, decision-making processes, and geographic knowledge systems. This is a big part of software computing. Wisdom engineering also falls into connection with mathematical reasoning as well as a strong concern with cognitive science and social-cognitive engineering, where intelligence is generated by socio-cognitive aggregates (mainly human beings) and structured according to the way humans thought and logic operates. Since then, information engineering has been an essential technology for knowledge incorporation. In the end, the exponentially increasing World Wide Web generates a growing market for improved usage of knowledge and technological advancement.

1.2 Knowledge and Knowledge Engineering

1.2.1 Knowledge

Knowledge is characterized as abilities, (i) which the person gains in terms of practice or learning; theoretical or practical knowledge of a subject; (ii) what is known inside or as a whole; facts and information; or (iii) knowing or acquainted with the experience of life or situation. This knowledge is defined accordingly. The retrieval of information requires complex cognitive processes including memory, understanding, connectivity, association, and reasoning. Knowledge of a subject and its capacity for usage for a specific purpose are also used to suggest trustworthy comprehension. Information may be divided into two forms of knowledge: tacit and clear. Tacit knowledge is the awareness that people have and yet cannot get. Tacit knowledge is more relevant since it gives people, locations, feelings, and memories a framework. Efficient tacit information transfer typically requires intensive intimate correspondence and trust. The tacit understanding is not easily shared. Still, consciousness comprises patterns and culture that we still cannot comprehend. On the other hand, information, which is easy to articulate, is called explicit knowledge. Coding or codification is the tool used to translate tacit facts into specific details. The awareness expressed, codified, and stored in such media is explicitly facts. Explicit information. The most common simple knowledge sources are guides, manuals, and protocols. Audio-visual awareness may also be an example of overt intelligence, which is based on the externalization of human skills, motivations, and knowledge.

1.2.2 Knowledge Engineering

Edward Feigenbaum and Pamela McCorduck created Knowledge Engineering in 1983: To address difficult issues that typically need a great deal of human experience in the fields of engineering, knowledge engineering needs the integration of information of computer systems. In engineering, design information is an essential aspect. If the information is collected and held in the knowledge base, important cost and output gains may be accomplished. In a range of fields, information base content may be used as to reuse information in other ways for diverse goals, to employ knowledge to create smart systems capable of carrying out complicated design work. We shall disseminate knowledge to other individuals within an organization. While the advantages of information capture and usage are obvious, it has long been known in the AI world that knowledge is challenging to access from specialists. Second, the specialists do not remember and describe “tacit knowledge” effectively, and this operates subconsciously and, where it is not impossible, is difficult to overcome the problems arising from several subject matters that they learn. To elaborate, they have to know what it is called....

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• 1 An Introduction to Knowledge Engineering and Data Analytics
• 2 A Framework for Big Data Knowledge Engineering
• 3 Big Data Knowledge System in Healthcare
• 4 Big Data for Personalized Healthcare
• 5 Knowledge Engineering for AI in Healthcare
• 6 Business Intelligence and Analytics from Big Data to Healthcare
• 7 Internet of Things and Big Data Analytics for Smart Healthcare
• 8 Knowledge-Driven and Intelligent Computing in Healthcare
• 9 Secure Healthcare Systems Based on Big Data Analytics
• 10 Predictive and Descriptive Analysis for Healthcare Data
• 11 Machine and Deep Learning Algorithms for Healthcare Applications
• 12 Artificial Intelligence in Healthcare Data Science with Knowledge Engineering
• 13 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System
• 14 Big Data in Healthcare: Management, Analysis, and Future Prospects
• 15 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System
• 16 Knowledge Fusion Patterns in Healthcare
• 17 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells
• 18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare
• Index
• End User License Agreement