Process Mining Techniques for Managing and Improving Healthcare Systems

Maha Zayoud

• Publisher: CRC Press
• Year: 2023
• Print ISBN: 9781032431093
• eBook ISBN: 9781000898583
• Topic: Computer Science
• Subtopic: Probability & Statistics
• Index: Computer Science

This book discusses a new process mining method along with a detailed comparison between different techniques that provide a complete vision of the process of data acquisition, data analysis, and data prediction.

Process Mining Techniques for Managing and Improving Healthcare Systems offers a new framework for process learning which is probabilistic and enables the process to be learned in an accumulative manner. The steps of prediction modeling and building the required knowledge are highlighted throughout the book, along with a strong emphasis on the correlation between the healthcare domain and technology including the different aspects, such as: managing records, information, and procedures; early detection of diseases; and the improvement of accuracy in choosing the right treatment procedures.

This reference provides a wealth of knowledge for practitioners, researchers, and students at the basic and intermediary levels working within the healthcare system, computer science, electronics and communications, as well as medical providers and also hospital management entities.

Table of contents

• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Contents
• List of Figures
• List of Tables
• List of Abbreviations
• Acknowledgments
• Preface
• Introduction
• Objectives and Overview
• Chapter 1 Healthcare System
• Chapter 2 Data Mining and Management Information Systems
• Chapter 3 Process Mining and Big Data in Healthcare
• Chapter 4 The Proposed β Algorithm for Healthcare System
• Chapter 5 Building a Hospital Management Platform
• Chapter 6 The Advantages of Using the MYL Platform
• Conclusion and Limitations
• Appendix A Appendix: The β Algorithms
• Appendix B Appendix: Rational Database
• Appendix C Appendix: The β Algorithm Using Hadoop Big Data Concept
• Appendix D Appendix: An Overview of the MYL Framework's Source Code
• Appendix E Appendix: MYL Software Output Examples
• References
• Index