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E-grāmata: Innovation in Health Informatics: A Smart Healthcare Primer

Edited by (Effat University, Computer Science Department, College of Engineering, Jeddah, Saudi Arabia), Edited by (Assistant Professor, Computer Science Department, College of Engineering, Deree College, The American College of Greece, Greece and Dean of)
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Innovation in Health Informatics: A Smart Healthcare PrimerPalielināt
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Innovation in Health Informatics: A Smart Healthcare Primer explains how the most recent advances in information and communication technologies have paved the way for new breakthroughs in healthcare. The book showcases current and prospective applications in a context defined by an imperative to deliver efficient, patient-centered and sustainable healthcare systems. Topics discussed include big data, medical data analytics, artificial intelligence, machine learning, virtual and augmented reality, 5g and sensors, Internet of Things, nanotechnologies and biotechnologies. Additionally, there is a discussion on social issues and policy- making for the implementation of smart healthcare.

This book is a valuable resource for undergraduate and graduate students, practitioners, researchers, clinicians and data scientists who are interested in how to explore the intersections between bioinformatics and health informatics.

  • Provides a holistic discussion on the new landscape of medical technologies, including big data, analytics, artificial intelligence, machine learning, virtual and augmented reality, 5g and sensors, Internet of Things, nanotechnologies and biotechnologies
  • Presents a case study driven approach, with references to real-world applications and systems
  • Discusses topics with a research-oriented approach that aims to promote research skills and competencies of readers
List of contributors xvii
Preface xxi
Acknowledgments xxv
Section A Smart Healthcare in the Era of Bid Data and Data Science 1(98)
Chapter 1 Smart Healthcare: emerging technologies, best practices, and sustainable policies
3(36)
Miltiadis D. Lytras
Paraskevi Papadopoulou
Akila Sarirete
1.1 Introduction
3(1)
1.2 Bridging innovative technologies and smart solutions in medicine and healthcare
4(6)
1.2.1 From genomics to proteomics to bioinformatics and health informatics
5(2)
1.2.2 Ways of developing intelligent and personalized healthcare interventions
7(1)
1.2.3 Advancing medicine and healthcare: insights and wise solutions
8(1)
1.2.4 Ways of disseminating our healthcare experience
8(2)
1.3 Visioning the future of resilient Smart Healthcare
10(1)
1.4 Content management resilient Smart Healthcare systems cluster
11(14)
1.4.1 Resilient Smart Healthcare learning management systems cluster
12(2)
1.4.2 Resilient Smart Healthcare document management systems cluster
14(3)
1.4.3 Resilient Smart Healthcare workflow automation
17(2)
1.4.4 Resilient Smart Healthcare microcontent services and systems
19(3)
1.4.5 Resilient Smart Healthcare collaboration systems and services
22(3)
1.5 Networking technologies for resilient Smart Healthcare systems cluster
25(1)
1.5.1 Smart systems
25(1)
1.6 Data warehouses and distributed systems for resilient Smart Healthcare applications
26(4)
1.6.1 Indicative smart applications for data warehouses in the context of resilient Smart Healthcare design
27(2)
1.6.2 Smart systems
29(1)
1.7 Analytics and business intelligence resilient Smart Healthcare systems cluster
30(2)
1.7.1 Indicative smart applications
31(1)
1.7.2 Smart systems
32(1)
1.8 Emerging technologies resilient Smart Healthcare systems cluster
32(2)
1.8.1 Indicative smart applications
32(2)
1.8.2 Smart systems
34(1)
1.9 Resilient Smart Healthcare innovation
34(2)
1.9.1 The evolution of resilient smart
34(1)
1.9.2 Indicative smart applications
35(1)
1.10 Conclusion
36(1)
References
36(1)
Further reading
37(2)
Chapter 2 Syndromic surveillance using web data: a systematic review
39(40)
Loukas Samaras
Elena Garcia-Barriocanal
Miguel-Angel Sicilia
2.1 Introduction: background and scope
39(2)
2.2 Methodology: research protocol and stages
41(4)
2.2.1 Stage 1: Preparation, research questions, and queries
41(2)
2.2.2 Stage 2: Data retrieval
43(1)
2.2.3 Stage 3: Data analysis: study selection and excluding criteria
43(1)
2.2.4 Stage 4: Data synthesis
43(1)
2.2.5 Stage 5: Results analysis
44(1)
2.2.6 Stage 6: Writing
44(1)
2.3 Results and analysis
45(9)
2.3.1 RQ1: Is the academic interest growing or declining?
45(1)
2.3.2 RQ2: Regarding syndromic surveillance using web data, what aspects have been explored until today in the available literature?
46(8)
2.3.3 RQ3: What topics need further development and research?
54(1)
2.4 Discussion and conclusions
54(7)
2.4.1 Results
54(1)
2.4.2 Information systems and epidemics
55(2)
2.4.3 Impact to society, ethics, and challenges
57(1)
2.4.4 Smart Healthcare innovations
58(1)
2.4.5 Conclusions and outlook
59(2)
2.5 Teaching assignments
61(1)
Acknowledgments
61(1)
Author contributions
61(1)
References
61(2)
Appendix: Included studies (alphabetical)
63(16)
Chapter 3 Natural Language Processing, Sentiment Analysis, and Clinical Analytics
79(20)
Adil Rajput
3.1 Introduction
79(2)
3.1.1 Natural Language Processing and Healthcare/Clinical Analytics
79(1)
3.1.2 Sentiment analysis
80(1)
3.2 Natural Language Processing
81(10)
3.2.1 Traditional approach-key concepts
81(5)
3.2.2 Statistical approach-key concepts
86(5)
3.3 Applications
91(3)
3.3.1 Sentiment analysis
91(1)
3.3.2 Natural Language processing application in medical sciences
92(2)
3.4 Conclusion
94(1)
3.4.1 Future research directions
94(1)
3.4.2 Teaching assignments
95(1)
References
95(2)
Further reading
97(2)
Section B Advanced Decision Making and Artificial Intelligence for Smart Healthcare 99(88)
Chapter 4 Clinical decision support for infection control in surgical care
101(22)
Marco Spruit
Sander van der Rijnst
4.1 Introduction
101(1)
4.2 Research methodology
102(2)
4.2.1 Data collection methods
103(1)
4.2.2 Design objectives
104(1)
4.3 Clinical decision support prototype
104(8)
4.3.1 Contextual background
105(2)
4.3.2 Describing the surgical process using process-deliverable diagrams
107(2)
4.3.3 Data sources, data collection procedure, and data description
109(1)
4.3.4 Algorithms
109(2)
4.3.5 Key performance indicators
111(1)
4.3.6 Opportunities for local improvements
112(1)
4.4 Exploratory data analysis
112(5)
4.4.1 Appropriate use of prophylactic antibiotics
113(1)
4.4.2 Maintenance of (perioperative) normothermia
113(1)
4.4.3 Hygienic discipline in operating rooms regarding door movements
114(3)
4.5 Discussion and implications
117(2)
4.5.1 Limitations and further research
118(1)
4.6 Conclusion
119(1)
4.7 Teaching assignments
120(1)
References
120(1)
Further reading
121(2)
Chapter 5 Human activity recognition using machine learning methods in a smart healthcare environment
123(22)
Abdulhamit Subasi
Kholoud Khateeb
Tayeb Brahimi
Akila Sarirete
5.1 Introduction
123(4)
5.2 Background and literature review
127(4)
5.2.1 Human activity recognition with body sensors
127(2)
5.2.2 Human activity recognition with mobile phone sensors
129(2)
5.3 Machine learning methods
131(4)
5.3.1 Artificial neural networks
131(1)
5.3.2 k-Nearest neighbor
131(1)
5.3.3 Support vector machine
132(1)
5.3.4 Naive Bayes
132(1)
5.3.5 Classification and regression tree
132(1)
5.3.6 C4.5 decision tree
133(1)
5.3.7 REPTree
133(1)
5.3.8 LADTree algorithm
133(1)
5.3.9 Random tree classifiers
134(1)
5.3.10 Random forests
134(1)
5.4 Results
135(5)
5.4.1 Experimental results for human activity recognition data taken from body sensors
136(2)
5.4.2 Experimental results for human activity recognition data taken from smartphone sensors
138(2)
5.5 Discussion and conclusion
140(2)
5.6 Teaching assignments
142(1)
References
142(3)
Chapter 6 Application of machine learning and image processing for detection of breast cancer
145(18)
Muhammad Kashif
Kaleem Razzaq Malik
Sohail Jabbar
Junaid Chaudhry
6.1 Introduction
145(4)
6.1.1 Mammograms
146(1)
6.1.2 Preprocessing
147(1)
6.1.3 Segmentation
147(1)
6.1.4 Machine learning
147(2)
6.2 Literature review
149(1)
6.3 Proposed work
150(7)
6.3.1 Dataset
150(1)
6.3.2 Noise removal (preprocessing)
150(2)
6.3.3 Segmentation process
152(1)
6.3.4 Feature extraction
153(2)
6.3.5 Training model and testing
155(1)
6.3.6 Classification
155(1)
6.3.7 Performance evaluation metrics
155(1)
6.3.8 f-Score measure
156(1)
6.4 Results
157(1)
6.5 Discussions
158(2)
6.6 Conclusion
160(1)
6.7 Research contribution highlights
161(1)
6.8 Teaching assignments
161(1)
References
162(1)
Chapter 7 Toward information preservation in healthcare systems
163(24)
Omar El Zarif
Ramzi A. Haraty
7.1 Introduction
163(2)
7.2 The literature review
165(3)
7.2.1 Log files
165(1)
7.2.2 Graph
166(1)
7.2.3 Clustering
167(1)
7.2.4 Matrices
167(1)
7.3 Our approach
168(9)
7.3.1 Background
169(1)
7.3.2 Adaptation to multilevel
170(7)
7.3.3 Complexity analysis
177(1)
7.4 Experimental results
177(6)
7.4.1 Performance results of the detection algorithm
178(2)
7.4.2 Performance results of the recovery algorithm
180(2)
7.4.3 Memory footprint analysis
182(1)
7.5 Conclusion
183(1)
7.6 Teaching assignments
184(1)
References
184(3)
Section C Emerging technologies and systems for smart healthcare 187(186)
Chapter 8 Security and privacy solutions for smart healthcare systems
189(28)
Yang Lu
Richard O. Sinnott
8.1 Introduction
189(2)
8.2 Smart healthcare framework and techniques
191(5)
8.3 Identified issues and solutions
196(14)
8.3.1 Authentication
198(4)
8.3.2 Privacy-aware access control
202(4)
8.3.3 Anonymization
206(4)
8.4 Discussion
210(1)
8.5 Conclusions and open research issues in future
211(1)
8.6 Teaching assignments
212(1)
References
212(4)
Further reading
216(1)
Chapter 9 Cloud-based health monitoring framework using smart sensors and smartphone
217(28)
Abdulhamit Subasi
Lejla Bandic
Saeed Mian Qaisar
9.1 Introduction
217(3)
9.2 Background and literature review
220(5)
9.2.1 Electrocardiogram in cloud-based mobile healthcare
221(2)
9.2.2 Electroencephalogram in cloud-based mobile healthcare
223(2)
9.3 Signal acquisition, segmentation, and denoising methods
225(3)
9.3.1 Adaptive rate acquisition
226(1)
9.3.2 Adaptive rate segmentation
226(1)
9.3.3 Adaptive rate interpolation
227(1)
9.3.4 Adaptive rate filtering
227(1)
9.4 Feature extraction methods
228(2)
9.4.1 Autoregressive Burg model for spectral estimation
229(1)
9.5 Machine learning methods
230(1)
9.6 Results
231(6)
9.6.1 Experimental results for electrocardiogram
233(2)
9.6.2 Experimental results for electroencephalogram
235(2)
9.7 Discussion and conclusion
237(2)
9.8 Teaching assignments
239(1)
References
239(6)
Chapter 10 Mobile Partogram-m-Health technology in the promotion of parturient's health in the delivery room
245(16)
Karla Maria Carneiro Rolim
Mo-ian Caltbpe Dantas Pinheiro
Pldcido Rogirio Pinheiro
Mirna Albuquerque Frota
Josi Eurico de Vasconcelos Filho
Izabela de Sousa Martins
Maria Solange Nogueira dos Santos
Firmina Hermelinda Saldanha Albuquerque
10.1 Introduction
246(2)
10.2 The Mobile Partogram conception-m-Health technology in parturient care in the delivery room
248(2)
10.3 Participatory user-centered interaction design to support and understand the conception of partograma mobile
250(1)
10.4 Identifying needs and defining requirements
251(3)
10.4.1 Design of alternatives
254(1)
10.5 Building an interactive version (high-fidelity prototype)
254(1)
10.6 Evaluation (usability)
255(1)
10.7 Final considerations
255(2)
10.8 Teaching assignments
257(1)
References
257(4)
Chapter 11 Artificial intelligence-assisted detection of diabetic retinopathy on digital fundus images: concepts and applications in the National Health Service
261(18)
Michael Kouroupis
Nikolaos Korfiatis
James Cornford
11.1 Introduction
261(1)
11.2 Diabetic retinopathy in the National Health Service
262(3)
11.3 Predictive analytics in diabetic retinopathy screening
265(5)
11.3.1 Big data in the context of diabetic retinopathy screening
266(1)
11.3.2 Predictive analytics in diagnostic retina screening
267(1)
11.3.3 Evaluation and performance measures
268(2)
11.4 Implementation in a smart healthcare setting
270(4)
11.4.1 Upskilling the workforce
270(2)
11.4.2 Multimodal imaging in diabetic retinopathy: integrating optical coherent tomography
272(2)
11.5 Challenges
274(1)
11.5.1 Adoption and clinical governance
274(1)
11.5.2 Ethical and legal compliance
274(1)
11.6 Conclusion
275(1)
References
275(4)
Chapter 12 Virtual reality and sensors for the next generation medical systems
279(26)
Felix Mata
Miguel Torres-Ruiz
Roberto Zagal-Flores
Marco Moreno-Ibarra
12.1 Introduction
279(3)
12.2 Related work
282(2)
12.3 The proposed methodology
284(10)
12.3.1 Postural analysis stage
286(3)
12.3.2 Virtual modeling stage
289(2)
12.3.3 Self-assessment stage
291(1)
12.3.4 Analysis and presentation stage
291(3)
12.4 Experimental results
294(6)
12.5 Conclusions and future work
300(1)
12.6 Teaching assignments
301(1)
Acknowledgments
301(1)
References
302(3)
Chapter 13 Portable smart healthcare solution to eye examination for diabetic retinopathy detection at an earlier stage
305(18)
Nighat Mir
Mohammad A.U. Khan
Mome Gul Hussain
13.1 Introduction
305(3)
13.2 Fundus eye images: the fundus photography and its acquisition
308(2)
13.3 Fundus eye imaging and problems
310(1)
13.4 Smartphone fundus cameras in the market
311(1)
13.4.1 Volk iNview
311(1)
13.4.2 Peek vision
311(1)
13.4.3 D-EYE smartphone-based retinal imaging system
311(1)
13.4.4 ODocs eye care
311(1)
13.5 What is the problem?
312(1)
13.6 Impact of the problem
313(1)
13.7 Proposed solution
314(1)
13.8 Methodology and validation
314(2)
13.9 Popular ridge detectors for vessel segmentation
316(1)
13.10 Proposed method
316(1)
13.11 Experimental results
317(1)
13.12 Conclusion and future work
318(1)
13.13 Teaching assignments
319(1)
References
319(2)
Further reading
321(2)
Chapter 14 Improved nodule detection in chest X-rays using principal component analysis filters
323(30)
Mohammad A.U. Khan
Nighat Mir
Fahad Hameed Ahmad
14.1 Introduction
323(4)
14.2 Looking at rib structure from signal processing point-of-view
327(7)
14.3 Data acquisition
334(1)
14.4 System design
335(4)
14.4.1 Local normalization
336(1)
14.4.2 Multiscale nodule detection
337(1)
14.4.3 Detection of nodules in discrete X-ray images
338(1)
14.5 Experiment
339(3)
14.6 Results
342(3)
14.7 Implication of automated lung nodules detection for future generation medical systems
345(1)
14.8 Discussion and conclusion
346(1)
14.9 Teaching assignments
347(1)
References
347(2)
Further reading
349(4)
Chapter 15 Characterizing internet of medical things/personal area networks landscape
353(20)
Adil Rajput
Tayeb Brahimi
15.1 Introduction
353(2)
15.1.1 Internet of medical things and health informatics
353(1)
15.1.2 Personal area networks
354(1)
15.2 Architectural landscape
355(9)
15.2.1 Physical components
355(1)
15.2.2 Network component
356(8)
15.3 Prevalent interne of medical things applications
364(5)
15.3.1 Internet of medical things services and applications
364(3)
15.3.2 Internet of medical things companies leading the way
367(2)
15.4 Conclusions and future directions
369(1)
15.4.1 Future research directions
369(1)
15.4.2 Recommended assignments
369(1)
References
370(3)
Section D Social Issues and policy making for smart healthcare 373(30)
Chapter 16 Threats to patients' privacy in smart healthcare environment
375(20)
Samara M. Ahmed
Adil Rajput
16.1 Introduction
375(2)
16.2 Definitions
377(1)
16.3 Legislation and policy
378(4)
16.3.1 Privacy rule in Health Insurance and Portability Accountability Act
378(1)
16.3.2 Federal Information Security Management Act of 2002
379(1)
16.3.3 Cyber Enhancement Act 2014
380(1)
16.3.4 NIST Cyber Security Framework
381(1)
16.4 Typical smart healthcare architecture
382(4)
16.4.1 Network layer
382(3)
16.4.2 Technology layer
385(1)
16.4.3 Applications layer
385(1)
16.5 Typical security threats
386(4)
16.5.1 Attacks' classification
386(4)
16.6 Conclusion
390(1)
16.6.1 Future research directions
391(1)
16.6.2 Teaching assignments
391(1)
References
391(1)
Further reading
392(3)
Chapter 17 Policy implications for smart healthcare: the international collaboration dimension
395(8)
Miltiadis D. Lytras
Akila Sarirete
Vassilios Stasinopoulos
17.1 Introduction
395(1)
17.2 The smart healthcare utilization framework
395(4)
17.3 International collaboration for resilient smart healthcare
399(2)
References
401(1)
Further reading
402(1)
Index 403
Miltiadis D. Lytras is an expert in advanced computer science and management, with extensive experience in academia and the business sector in Europe and Asia. He is a Research Professor at Deree CollegeThe American College of Greece and a Distinguished Scientist at King Abdulaziz University, Saudi Arabia. Dr. Lytras specializes in cognitive computing, information systems, technology-enabled innovation, social networks, and knowledge management. He has coedited over 110 high-impact special issues in ISI/Scopus-indexed journals and authored more than 80 books with international publishers. Additionally, he has published over 120 high-impact papers in top-tier journals such as IEEE Transactions on Knowledge and Data Engineering and the Journal of Business Research. With 25 years of experience in Research and Development projects, Dr. Lytras has been involved in more than 70 R&D projects globally. He holds senior editorial positions in prestigious journals and is the Founding Editor and Editor in Chief of the International Journal on Semantic Web and Information Systems.

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.
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