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E-grāmata: Data Science and Medical Informatics in Healthcare Technologies

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This book highlights a timely and accurate insight at the endeavour of the bioinformatics and genomics clinicians from industry and academia to address the societal needs. The contents of the book unearth the lacuna between the medication and treatment in the current preventive medicinal and pharmaceutical system. It contains chapters prepared by experts in life sciences along with data scientists for examining the circumstances of health care system for the next decade. It also highlights the automated processes for analyzing data in clinical trial research, specifically for drug development. Additionally, the data science solutions provided in this book help pharmaceutical companies to improve on what had historically been manual, costly and laborious process for cross-referencing research in clinical trials on drug development, while laying the groundwork for use with a full range of other drugs for the conditions ranging from tuberculosis, to diabetes, to heart attacks and many others.

1 A Value of Data Science in the Medical Informatics: An Overview 1(16)
1.1 Introduction
1(1)
1.2 Predictive Analytics
2(3)
1.2.1 Analytics-as-a-Service
3(1)
1.2.2 Predictive Analytics in Healthcare
4(1)
1.2.3 Predictive Analytics and Healthcare
5(1)
1.2.4 Machine Learning's Role in Predictions
5(1)
1.3 The Role of Data Science in Healthcare
5(9)
1.3.1 Benefits of Data Science in Healthcare
7(1)
1.3.2 The Role of a Data Scientist in Healthcare
7(1)
1.3.3 Challenges of Data Science in Healthcare
7(1)
1.3.4 Applications of Data Science in Healthcare
8(4)
1.3.5 Future of Data Science in Healthcare Domain
12(2)
1.4 Conclusion
14(1)
References
14(3)
2 Data Science in Medical Informatics: Challenges and Opportunities 17(16)
2.1 Introduction
17(3)
2.1.1 Challenges
18(1)
2.1.2 Opportunities
19(1)
2.2 Data Science in Healthcare
20(8)
2.2.1 Data Analytics
24(1)
2.2.2 Real-Time Analytics
25(1)
2.2.3 Data Science Outlook in Healthcare Informatics
26(1)
2.2.4 Stages of Analytics
26(1)
2.2.5 Analytics Technologies
27(1)
2.3 Predictive Analytics
28(2)
2.3.1 Future of Predictive Analytics
29(1)
2.3.2 Reliability in Analytics
30(1)
2.4 Conclusion
30(1)
References
31(2)
3 Eminent Role of Machine Learning in the Healthcare Data Management 33(16)
3.1 Introduction
33(2)
3.2 Machine Learning in Healthcare
35(1)
3.2.1 Machine Learning in Medical Field
35(1)
3.3 Healthcare Data Management and Data Infrastructure
36(5)
3.3.1 Healthcare Data Ownership and Portability
37(1)
3.3.2 Data Science and Machine Learning Applications in Healthcare
38(2)
3.3.3 Machine Learning and Predictive Analytics
40(1)
3.4 Healthcare Informatics
41(2)
3.4.1 Machine Learning Approaches for Medical Informatics
43(1)
3.5 Challenges in Healthcare Data Analytics in Healthcare
43(3)
3.6 Conclusion
46(1)
References
46(3)
4 Potential and Adoption of Data Science in the Healthcare Analytics 49(20)
4.1 Introduction
49(4)
4.1.1 Challenges
51(1)
4.1.2 Possibilities
52(1)
4.2 The Utility of Data Science in Healthcare
53(4)
4.2.1 Why Healthcare Analytics?
53(1)
4.2.2 The Healthcare Analytics Adoption Model
53(1)
4.2.3 The Nine Levels of the Analytics Adoption Model [ 8]
54(3)
4.3 Data Science Use Cases in Healthcare
57(3)
4.4 Data Analytics
60(4)
4.4.1 Future of Data Science in Healthcare
61(1)
4.4.2 Stages of Analytics
62(1)
4.4.3 Healthcare Data Analytics Platforms
63(1)
4.4.4 Using Analytics to Address Chronic Disease Management
63(1)
4.5 Prospective Analysis for Predictive Model
64(3)
4.5.1 Future of Prospective Analysis for Predictive Model
65(1)
4.5.2 Reliability in Analytics
65(1)
4.5.3 Concerns of Prospective Analytics
66(1)
4.5.4 Benefits of Prospective Analysis
66(1)
4.6 Conclusion
67(1)
References
67(2)
5 Emerging Advancement of Data Science in the Healthcare Informatics 69
5.1 Introduction
69(3)
5.1.1 What is Healthcare Analytics and Why Does It Matter?
71(1)
5.1.2 Why Healthcare Analytics?
71(1)
5.2 The Use of Data Science in Healthcare
72(3)
5.3 Emerging Approaches to Advance Healthcare Data Analytics
75(3)
5.3.1 Data Analytics in Healthcare
76(1)
5.3.2 Outlook of Data Science in Healthcare Domain
77(1)
5.4 Importance of Advanced Analytics in Healthcare
78(5)
5.4.1 The Value of Data in Analytics
79(1)
5.4.2 Analytics for Transforming Healthcare
79(2)
5.4.3 The Significance of Analytics in Various Fields
81(2)
5.5 Data Analytics Using Predictive Model in Healthcare
83(2)
5.5.1 Benefits of Predictive Model in Healthcare [ 18]
83(1)
5.5.2 Clinical Predictive Analytics Market
84(1)
5.5.3 Concerns of Predictive Analytics
84(1)
5.5.4 Benefits of Predictive Analytics
84(1)
5.6 Conclusion
85(1)
References
86
Nguyen Thi Dieu Linh, PhD, is Dy. Head of Science and Technology Department, Hanoi University of Industry, Hanoi, Vietnam (HaUI). She has more than 19 years of academic experience in electronics, IoT, smart gardens, and telecommunication. She received her Ph.D. in Information and Communications Engineering from Harbin Institute of Technology, Harbin, China, in 2013. She is Chair of the International Conference of Intelligent System and Network. She has edited book titles Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches and Distributed Artificial Intelligence: A Modern Approach by T&F. She serves as an Editorial Board Member of several journals. She has chaired many technical events in different universities in Viet Nam.





Joan Lu is Professor of Informatics at the University of Huddersfield (UK). Her extensive research covers information access, retrieval and visualization, XML technology, object-oriented technologies, agent technology, data management systems, security issues and Internet computing. She has been Invited Speaker for industrial-oriented events and published 5 academic books and more than 160 journal articles. Professor Lu has acted as Founder and Program Chair for the International XML Technology workshop and XMLTech (USA) for 11 years (20032011). She also serves as Chair of 5 separate international conferences, is Regular Reviewer for several international journals and Committee Member for 16 international conferences. She specializes in XML technology and mobile computing with image retrieval through the latest wireless devices. Professor Lu serves as Member of the British Computer Society (BCS), BCS Examiner of Advanced Database Management Systems and Fellow of the Higher Education Academy (UK). She is Founder and Editor-in-Chief for the International Journal of Information Retrieval Research.