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Artificial Intelligence and Big Data Analytics for Smart Healthcare [Mīkstie vāki]

Edited by (Assistant Professor, Computer Science Department, College of Engineering, Deree College, The American College of Greece, Greece and Dean of Graduate Studies an), Edited by , Edited by , Edited by (Research Professor, Deree College, The American College of Greece, Greece)
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Artificial Intelligence and Big Data Analytics for Smart HealthcarePalielināt
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Permanent link: https://www.kriso.lv/db/9780128220603.html
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Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate.

The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry.

  • Presents a holistic discussion on the new landscape of data driven medical technologies including Big Data, Analytics, Artificial Intelligence, Machine Learning, and Precision Medicine
  • Discusses such technologies with case study driven approach with reference to real world application and systems, to make easier the understanding to the reader not familiar with them
  • Encompasses an international collaboration perspective, providing understandable knowledge to professionals involved with healthcare to leverage productive partnerships with technology developers
List of contributors xiii
Preface: artificial intelligence and big data analytics for smart healthcare: a digital transformation of healthcare primer xvii Acknowledgments xxix
1 Healthcare in the times of artificial intelligence: setting a value-based context 1(10)
Dimitrios M. Lytras
Hara Lytra
Miltiadis D. Lytras
1.1 Introduction-mapping the current challenges in the health domain
1(2)
1.2 Value-based approach to healthcare
3(2)
1.3 Current state of artificial intelligence utilization in the health domain/artificial intelligence metaphors and its contribution to the digital transformation of healthcare
5(3)
1.4 Conclusion
8(1)
References
8(1)
Further reading
9(2)
2 High-level strategy for implementing artificial intelligence at the Saudi Commission for Health Specialties 11(14)
Abdulrahman Housawi
Basim Alsaywid
Miltiadis D. Lytras
Areti Apostolaki
Abrar W. Tolah
Maha Abuzenada
Manal Hassan Almehda
2.1 Introduction
11(3)
2.2 Literature review
14(3)
2.3 Current state of AI utilization at the SCFHS
17(4)
2.3.1 Matching prospective trainees (residents) to residency training programs
17(1)
2.3.2 Professional accreditation of health-care practitioners
17(1)
2.3.3 ML for recommending (individualized) professional development activities and programs
18(1)
2.3.4 The utility of natural language processing to improve performance at the SCFHS
18(1)
2.3.5 The utility of robotics/RPA to improve performance at the SCFHS
19(2)
2.4 AI implementation is an opportunity for successful human-machine collaboration
21(1)
2.5 Conclusion and ethical considerations
21(1)
References
21(2)
Further reading
23(2)
3 Big data infrastructure: data mining, text mining, and citation context analysis in scientific literature 25(22)
Usman Ahmad
Mohammed Alsayer
Salem Alelyani
Iqra Safder
Sehrish Iqbal
Saeed-Ul Hassan
Naif Radi Aljohani
3.1 Introduction
25(3)
3.2 Literature review
28(4)
3.3 Data and methodology
32(4)
3.3.1 Data and preprocessing
33(1)
3.3.2 Feature engineering
34(2)
3.4 Results and discussion
36(4)
3.4.1 Training and testing data
36(1)
3.4.2 Discussion of ROC curves
37(1)
3.4.3 Discussion on precision-recall curves
38(1)
3.4.4 Discussion on important features
38(1)
3.4.5 Evaluation
39(1)
3.5 Concluding remarks
40(1)
Acknowledgment
40(1)
Appendix A
40(2)
References
42(5)
4 Place attachment theories: a spatial approach to smart health and healing 47(16)
Sarah Jorgensen
Vasiliki Geropanta
4.1 Introduction-smart healthcare, smart-home services, and the place attachment theory
47(3)
4.1.1 Contributions
49(1)
4.1.2 Linking this study to artificial intelligence and big data analytics
49(1)
4.2 Literature review-using place attachment to define "home"
50(2)
4.2.1 Home as a place for healing
50(1)
4.2.2 Place attachment and the home environment
51(1)
4.3 Methodology-case studies
52(5)
4.3.1 Case study 1-smart lighting
53(1)
4.3.2 Case study 2-IoT connectivity of devices
54(1)
4.3.3 Case study 3-personalization of spaces
55(2)
4.4 Implementation
57(1)
4.4.1 A scenario of implementing the three case studies-St George's Hospital, Port Elizabeth, and a three-dimensional analysis
57(1)
4.5 Conclusion and recommendations
58(1)
4.6 Future research
59(1)
References
59(4)
5 Utilizing loT-based sensors and prediction model for health-care monitoring system 63(18)
Ganjar Alfian
Muhammad Syafrudin
Norma Latif Fitriyani
M. Alex Syaekhoni
Jongtae Rhee
5.1 Introduction
63(2)
5.2 Literature review
65(3)
5.3 Health-care monitoring system
68(7)
5.3.1 System design and implementation
68(2)
5.3.2 Blood glucose prediction model
70(5)
5.4 Result and discussion
75(3)
5.4.1 Health-care monitoring system
75(1)
5.4.2 Blood glucose prediction model
76(2)
5.5 Conclusion
78(1)
Acknowledgment
78(1)
References
78(3)
6 QoS of mobile cloud computing applications in healthcare 81(16)
Jesus Peral
Victor Sanchez
Margarita Guerrero
Higinio Mora
David Gil
6.1 Introduction
81(4)
6.2 Cloud computing and mobile cloud computing
85(1)
6.3 QoS in CC and MCC
86(1)
6.4 CC and MCC applications in the health area
87(3)
6.5 New trends of security of CC in the health area
90(1)
6.6 Evaluation of performance and QoS in the health area
91(3)
6.7 Conclusion
94(1)
Acknowledgments
94(1)
References
95(2)
7 Analysis of Parkinson's disease based on mobile application 97(24)
Miguel Torres-Ruiz
Giovanni Guzman
Marco Moreno-Ibarra
Ana Acosta-Arenas
7.1 Introduction
97(3)
7.2 Related work
100(3)
7.3 Methods and materials
103(7)
7.3.1 Monitoring and data collection
103(4)
7.3.2 Data preprocessing
107(3)
7.4 Experimental results
110(6)
7.4.1 The manual dexterity activity
111(1)
7.4.2 The walking activity
111(3)
7.4.3 The memory activity
114(2)
7.5 Conclusion and future work
116(1)
Acknowledgments
117(1)
References
117(4)
8 Mobile Partogram-m-Health technology in the promotion of parturient's health in the delivery room 121(14)
Karla Maria Cameiro Rolim
Miirianan Caliope Dantas Pinheiro
Placido Rogerio Pinheiro
Mirna Albuquerque Frota
Jose Eurico de Vasconcelos Filho
Izabela de Sousa Martins
Maria Solange Nogueira dos Santos
Firmina Hermelinda Saldanha Albuquerque
8.1 Introduction
121(2)
8.2 The Mobile Partogram conception-m-Health technology in parturient care in the delivery room
123(2)
8.3 Participatory user-centered interaction design to support and understand the conception of partograma mobile
125(1)
8.4 Identifying needs and defining requirements
126(3)
8.4.1 Design of alternatives
129(1)
8.5 Building an interactive version (high-fidelity prototype)
129(1)
8.6 Evaluation (usability)
130(1)
8.7 Final considerations
130(1)
8.8 Teaching assignments
131(1)
References
132(3)
9 Self-evaluation mobile application on mild cognitive impairment based on Mini-Mental State Examination with bilingual support 135(10)
Lap-Kei Lee
Yin-Chun Fung
Nga-In Wu
Ka-Yuen Leung
Tsz-Kin Tsang
Chun-Ho Ho
9.1 Introduction
135(1)
9.1.1 Our contribution
136(1)
9.2 Overview of the Mini-Mental State Examination
136(1)
9.3 Our mobile application
137(3)
9.3.1 Overview of the solution
137(1)
9.3.2 User interface design for seniors and the elderly
138(1)
9.3.3 Question types of the evaluation
138(1)
9.3.4 Record tracking
139(1)
9.4 Preliminary evaluation
140(2)
9.4.1 Evaluation with users
140(1)
9.4.2 Discussion with selected users
141(1)
9.4.3 Feedbacks from nursing domain experts
142(1)
9.5 Conclusion and future enhancement
142(1)
References
143(2)
10 Spatiotemporal Big Data-Driven Vessel Traffic Risk Estimation for Promoting Maritime Healthcare: Lessons Learnt from Another Domain than Healthcare 145(16)
Zikun Feng
Yan Li
Zhao Liu
Ryan Wen Liu
10.1 Introduction
145(3)
10.2 Ship domain
148(1)
10.3 Proposed method
149(5)
10.3.1 Trajectory data interpolation
149(2)
10.3.2 Cross area calculation of ship domain
151(2)
10.3.3 Ship collision risk assessment
153(1)
10.4 Experimental results and analysis
154(3)
10.4.1 The verification of Monte Carlo probabilistic algorithm
155(1)
10.4.2 Simulate three situations of ship behavior
155(1)
10.4.3 AIS data experiment
155(2)
10.5 Conclusion
157(1)
References
158(3)
11 Neurofeedback using video games for attention-deficit/hyperactivity disorder 161(16)
Nighat Mir
Muhammad Asmatullah Khan
Yumna Ansari
11.1 Introduction
161(1)
11.2 Problems of ADHD
162(1)
11.3 Background
163(10)
11.3.1 Why neurofeedback
163(1)
11.3.2 Limitations of neurofeedback
164(1)
11.3.3 Treatments of ADHD
164(1)
11.3.4 Supportive treatments
165(1)
11.3.5 Neurofeedback training
166(1)
11.3.6 Neurofeedback treatment protocols
167(1)
11.3.7 Hypothesis
168(1)
11.3.8 Data collection
169(3)
11.3.9 Game architecture
172(1)
11.4 Conclusion and future recommendations
173(1)
References
173(2)
Further reading
175(2)
12 Medical diagnosis in Alzheimer's disease based on supervised and semisupervised learning 177(20)
Mingbo Zhao
Yuan Gao
Zhao Zhang
Bing Li
12.1 Introduction
177(2)
12.2 Notations and review of related work
179(2)
12.2.1 Notations
179(1)
12.2.2 Linear discriminant analysis
179(1)
12.2.3 Review of graph-based semisupervised learning
180(1)
12.3 Trace ratio linear discriminant analysis for medical diagnosis: a case study of dementia via supervised learning
181(3)
12.3.1 An improved algorithms for solving the trace ratio problem of TR-LDA
181(3)
12.4 Identifying demented patients via TR-LDA
184(1)
12.4.1 Data descriptions
184(1)
12.4.2 Prediction stage
184(1)
12.5 Simulations
185(2)
12.5.1 Diagnosis results
185(1)
12.5.2 Visualization
186(1)
12.6 Compact graph-based semisupervised learning for medical diagnosis in Alzheimer's disease: a case study of dementia via semisupervised learning
187(6)
12.6.1 Review of graph construction
187(3)
12.6.2 Identifying demented patients via compact graph semisupervised learning
190(1)
12.6.3 Simulation
191(2)
12.7 Conclusion
193(1)
References
193(2)
Further reading
195(2)
13 A support vector machine-based voice disorders detection using human voice signal 197(12)
Pak Ho Leung
Kwok Tai Chui
Kenneth Lo
Patricia Ordonez de Pablos
13.1 Introduction
197(1)
13.2 Literature review
198(1)
13.3 Methodology of support vector machine-based voice disorders detection
199(2)
13.3.1 Programming tool
199(1)
13.3.2 Voice ICar Federico II (VOICED) database
199(1)
13.3.3 Feature extraction
200(1)
13.3.4 Voice disorders detection using support vector machine
200(1)
13.4 Performance evaluation of proposed support vector machine algorithm for voice disorders detection
201(1)
13.5 Research challenges of smart health-care applications
202(3)
13.5.1 Data collection
203(1)
13.5.2 Data selection
203(1)
13.5.3 Expenditure
203(1)
13.5.4 New knowledge and skills to learn
203(1)
13.5.5 Urban versus rural health
203(1)
13.5.6 Linked databases
204(1)
13.5.7 Optimizing treatment
204(1)
13.5.8 Privacy
204(1)
13.6 Research limitations and future research directions
205(1)
13.7 Visions and conclusion
205(1)
References
206(3)
14 COVID-19 detection from X-ray images using artificial intelligence 209(16)
Abdulhamit Subasi
Saqib Ahmed Qureshi
Tayeb Brahimi
Akila Serireti
14.1 Introduction
209(4)
14.2 Deep learning in COVID-19 prognosis using X-ray images
213(4)
14.3 Classification methods
217(1)
14.3.1 Convolutional neural networks
217(1)
14.3.2 Transfer learning
218(1)
14.4 Results and discussion
218(3)
14.4.1 Dataset
218(1)
14.4.2 Experimental setup
219(1)
14.4.3 Performance measures
219(1)
14.4.4 Experimental results
219(1)
14.4.5 Discussion
220(1)
14.5 Conclusion
221(1)
References
222(3)
15 Empowering the One Health approach and health resilience with digital technologies across OECD countries: the case of COVID-19 pandemic 225(18)
Paraskevi Papadopoulou
Miltiadis D. Lytras
15.1 Introduction
225(4)
15.2 Aims and methodology of this study
229(1)
15.3 Findings and suggestions regarding the research questions
229(9)
15.3.1 The COVIDI9 case in OECD countries: some background information
229(1)
15.3.2 Digital technologies in the service of health and healthcare
230(5)
15.3.3 Multidimensional framework and future recommendations
235(3)
15.4 Conclusion
238(1)
References
239(2)
Further reading
241(2)
16 An overview of artificial intelligence and big data analytics for smart healthcare: requirements, applications, and challenges 243(12)
Kwok Tai Chui
Miltiadis D. Lytras
Anna Visvizi
Akila Sarirete
16.1 Introduction
243(3)
16.2 Requirements of smart health-care applications
246(1)
16.2.1 Mission critical applications
246(1)
16.2.2 Scalable design
246(1)
16.2.3 Cost-effective design
246(1)
16.2.4 User-centered design
247(1)
16.3 Smart health-care applications using AI and BDA techniques
247(2)
16.3.1 Health-care monitoring and keeping well
247(1)
16.3.2 Disease diagnosis and prediction
247(1)
16.3.3 Drug discovery and development
248(1)
16.3.4 Intensive care
248(1)
16.3.5 Education and training
249(1)
16.4 Challenges
249(2)
16.4.1 Large-scale open health-care data
249(1)
16.4.2 Technology transfer
250(1)
16.4.3 Public acceptance in AI- and BDA-based applications
250(1)
16.4.4 Policy establishment
251(1)
16.5 Conclusion
251(1)
References
251(4)
Index 255
Miltiadis D. Lytras is an expert in advanced computer science and management, editor, lecturer, and research consultant, with extensive experience in academia and the business sector in Europe and Asia. Dr. Lytras is a Research Professor at Deree College - The American College of Greece and a Distinguished Scientist at the King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia. Dr. Lytras is a world-class expert in the fields of cognitive computing, information systems, technology enabled innovation, social networks, computers in human behavior, and knowledge management. In his work, Dr. Lytras seeks to bring together and exploit synergies among scholars and experts committed to enhancing the quality of education for all. Dr. Akila Sarirete is Dean of Graduate Studies and Research at Effat University. She received her PhD degree in Computer Science and Knowledge Management. Her main research interests are in artificial intelligence, knowledge management, communities of practice, machine learning, big data, and service-oriented architectures. She presented her research work in several conferences in different countries. Dr. Sarirete has a vast experience in software development industry and software engineering. She is interested in engineering education, innovation, smart cities and villages especially, the human aspect and the collaborative work. Anna Visvizi is an economist and political scientist, editor, and research and political consultant with extensive experience in academia, think tank and government sectors in Europe and the United States. Associate Professor at SGH Warsaw School of Economics, Warsaw, Poland, and Visiting Scholar at Effat University, Jeddah, Saudi Arabia, Professor Visvizis expertise covers issues pertinent to the intersection of politics, economics, and ICT. This translates in her research and advisory roles in the fields of AI and geopolitics, smart cities and smart villages, knowledge and innovation management, and technology diffusion, especially with regard to the EU and BRI. Dr. Kwok Tai Chui received the B.Eng. degree in electronic and communication engineering Business Intelligence Minor, with First-class Honor, and PhD degree from City University of Hong Kong in 2013 and 2017 respectively. He was the recipient of 2nd Prize Award (Postgraduate Category) of 2014 IEEE Region 10 Student Paper Contest. Also, he received Best Paper Award in IEEE The International Conference on Consumer Electronics-China, in both 2014 and 2015. He has published more than 30 research publications in edited book, referred international journals, conferences and book chapters. His research interests include wireless communication, pattern recognition, healthcare, machine learning algorithms and optimization. He has served as various editorial positions and guest editors in referred international journals and conferences.