Innovation in Health Informatics: A Smart Healthcare Primer

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Innovation in Health Informatics: A Smart Healthcare Primer
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Contents
	Section A Smart Healthcare in the Era of Bid Data and Data Science1
	Section B Advanced Decision Making and Artificial Intelligence for Smart Healthcare99
	Section C Emerging technologies and systems for smart healthcare187
	Section D Social Issues and policy making for smart healthcare373
List of contributors
Preface
Acknowledgments
Section A: Smart Healthcare in the Era of Bid Data and Data Science
1 Smart Healthcare: emerging technologies, best practices, and sustainable policies
	1.1 Introduction
	1.2 Bridging innovative technologies and smart solutions in medicine and healthcare
		1.2.1 From genomics to proteomics to bioinformatics and health informatics
		1.2.2 Ways of developing intelligent and personalized healthcare interventions
		1.2.3 Advancing medicine and healthcare: insights and wise solutions
		1.2.4 Ways of disseminating our healthcare experience
	1.3 Visioning the future of resilient Smart Healthcare
	1.4 Content management resilient Smart Healthcare systems cluster
		1.4.1 Resilient Smart Healthcare learning management systems cluster
		1.4.2 Resilient Smart Healthcare document management systems cluster
		1.4.3 Resilient Smart Healthcare workflow automation
		1.4.4 Resilient Smart Healthcare microcontent services and systems
		1.4.5 Resilient Smart Healthcare collaboration systems and services
	1.5 Networking technologies for resilient Smart Healthcare systems cluster
		1.5.1 Smart systems
	1.6 Data warehouses and distributed systems for resilient Smart Healthcare applications
		1.6.1 Indicative smart applications for data warehouses in the context of resilient Smart Healthcare design
		1.6.2 Smart systems
	1.7 Analytics and business intelligence resilient Smart Healthcare systems cluster
		1.7.1 Indicative smart applications
		1.7.2 Smart systems
	1.8 Emerging technologies resilient Smart Healthcare systems cluster
		1.8.1 Indicative smart applications
		1.8.2 Smart systems
	1.9 Resilient Smart Healthcare innovation
		1.9.1 The evolution of resilient smart
		1.9.2 Indicative smart applications
	1.10 Conclusion
	References
	Further reading
2 Syndromic surveillance using web data: a systematic review
	2.1 Introduction: background and scope
	2.2 Methodology: research protocol and stages
		2.2.1 Stage 1: Preparation, research questions, and queries
		2.2.2 Stage 2: Data retrieval
		2.2.3 Stage 3: Data analysis: study selection and excluding criteria
		2.2.4 Stage 4: Data synthesis
		2.2.5 Stage 5: Results analysis
		2.2.6 Stage 6: Writing
	2.3 Results and analysis
		2.3.1 RQ1: Is the academic interest growing or declining?
		2.3.2 RQ2: Regarding syndromic surveillance using web data, what aspects have been explored until today in the available li...
			2.3.2.1 Which diseases have been explored?
			2.3.2.2 Where did studies take place (region, country)?
			2.3.2.3 What is the web data source used or mentioned?
			2.3.2.4 What is the method(s) used for analysis and interpretation of the data?
			2.3.2.5 How many scientists have worked so far?
		2.3.3 RQ3: What topics need further development and research?
	2.4 Discussion and conclusions
		2.4.1 Results
		2.4.2 Information systems and epidemics
		2.4.3 Impact to society, ethics, and challenges
		2.4.4 Smart Healthcare innovations
		2.4.5 Conclusions and outlook
	2.5 Teaching assignments
	Acknowledgments
	Author contributions
	References
	Appendix: Included studies (alphabetical)
3 Natural Language Processing, Sentiment Analysis, and Clinical Analytics
	3.1 Introduction
		3.1.1 Natural Language Processing and Healthcare/Clinical Analytics
		3.1.2 Sentiment analysis
	3.2 Natural Language Processing
		3.2.1 Traditional approach—key concepts
			3.2.1.1 Preprocessing/tokenization
			3.2.1.2 Lexical analysis
			3.2.1.3 Syntactical analysis
			3.2.1.4 Semantic analysis
		3.2.2 Statistical spproach—key concepts
			3.2.2.1 Corpus and its intricacies
				3.2.2.1.1 Size
				3.2.2.1.2 Balance
				3.2.2.1.3 Representativeness
			3.2.2.2 Part-of-Speech tagging
			3.2.2.3 Treebank annotation
	3.3 Applications
		3.3.1 Sentiment analysis
		3.3.2 Natural Language processing application in medical sciences
	3.4 Conclusion
		3.4.1 Future research directions
		3.4.2 Teaching assignments
	References
	Further reading
Section B: Advanced Decision Making and Artificial Intelligence for Smart Healthcare
4 Clinical decision support for infection control in surgical care
	4.1 Introduction
	4.2 Research methodology
		4.2.1 Data collection methods
		4.2.2 Design objectives
	4.3 Clinical decision support prototype
		4.3.1 Contextual background
		4.3.2 Describing the surgical process using process-deliverable diagrams
		4.3.3 Data sources, data collection procedure, and data description
		4.3.4 Algorithms
		4.3.5 Key performance indicators
		4.3.6 Opportunities for local improvements
	4.4 Exploratory data analysis
		4.4.1 Appropriate use of prophylactic antibiotics
		4.4.2 Maintenance of (perioperative) normothermia
		4.4.3 Hygienic discipline in operating rooms regarding door movements
	4.5 Discussion and implications
		4.5.1 Limitations and further research
	4.6 Conclusion
	4.7 Teaching assignments
	References
	Further reading
5 Human activity recognition using machine learning methods in a smart healthcare environment
	5.1 Introduction
	5.2 Background and literature review
		5.2.1 Human activity recognition with body sensors
		5.2.2 Human activity recognition with mobile phone sensors
	5.3 Machine learning methods
		5.3.1 Artificial neural networks
		5.3.2 k-Nearest neighbor
		5.3.3 Support vector machine
		5.3.4 Naïve Bayes
		5.3.5 Classification and regression tree
		5.3.6 C4.5 decision tree
		5.3.7 REPTree
		5.3.8 LADTree algorithm
		5.3.9 Random tree classifiers
		5.3.10 Random forests
	5.4 Results
		5.4.1 Experimental results for human activity recognition data taken from body sensors
			5.4.1.1 Dataset information
			5.4.1.2 Experimental results
		5.4.2 Experimental results for human activity recognition data taken from smartphone sensors
			5.4.2.1 Dataset information
			5.4.2.2 Experimental results
	5.5 Discussion and conclusion
	5.6 Teaching assignments
	References
6 Application of machine learning and image processing for detection of breast cancer
	6.1 Introduction
		6.1.1 Mammograms
		6.1.2 Preprocessing
		6.1.3 Segmentation
		6.1.4 Machine learning
			6.1.4.1 Supervised machine learning
				6.1.4.1.1 Classification
				6.1.4.1.2 Regression
			6.1.4.2 Unsupervised learning
				6.1.4.2.1 Clustering
				6.1.4.2.2 Association
			6.1.4.3 Semisupervised learning
			6.1.4.4 Reinforcement and deep learning
	6.2 Literature review
	6.3 Proposed work
		6.3.1 Dataset
		6.3.2 Noise removal (preprocessing)
		6.3.3 Segmentation process
		6.3.4 Feature extraction
		6.3.5 Training model and testing
		6.3.6 Classification
		6.3.7 Performance evaluation metrics
		6.3.8 f-Score measure
	6.4 Results
	6.5 Discussions
	6.6 Conclusion
	6.7 Research contribution highlights
	6.8 Teaching assignments
	References
7 Toward information preservation in healthcare systems
	7.1 Introduction
	7.2 The literature review
		7.2.1 Log files
		7.2.2 Graph
		7.2.3 Clustering
		7.2.4 Matrices
	7.3 Our approach
		7.3.1 Background
		7.3.2 Adaptation to multilevel
		7.3.3 Complexity analysis
	7.4 Experimental results
		7.4.1 Performance results of the detection algorithm
		7.4.2 Performance results of the recovery algorithm
		7.4.3 Memory footprint analysis
	7.5 Conclusion
	7.6 Teaching assignments
	References
Section C: Emerging technologies and systems for smart healthcare
8 Security and privacy solutions for smart healthcare systems
	8.1 Introduction
	8.2 Smart healthcare framework and techniques
	8.3 Identified issues and solutions
		8.3.1 Authentication
			8.3.1.1 Internet of Things authentication
			8.3.1.2 User authentication
			8.3.1.3 Distributed authentication
		8.3.2 Privacy-aware access control
			8.3.2.1 Patient-centric access control
			8.3.2.2 Staff access control
			8.3.2.3 Break-glass access control
		8.3.3 Anonymization
			8.3.3.1 Statistical disclosure control
			8.3.3.2 Privacy-preserving big data
	8.4 Discussion
	8.5 Conclusions and open research issues in future
	8.6 Teaching assignments
	References
	Further reading
9 Cloud-based health monitoring framework using smart sensors and smartphone
	9.1 Introduction
	9.2 Background and literature review
		9.2.1 Electrocardiogram in cloud-based mobile healthcare
		9.2.2 Electroencephalogram in cloud-based mobile healthcare
	9.3 Signal acquisition, segmentation, and denoising methods
		9.3.1 Adaptive rate acquisition
		9.3.2 Adaptive rate segmentation
		9.3.3 Adaptive rate interpolation
		9.3.4 Adaptive rate filtering
	9.4 Feature extraction methods
		9.4.1 Autoregressive Burg model for spectral estimation
	9.5 Machine learning methods
	9.6 Results
		9.6.1 Experimental results for electrocardiogram
		9.6.2 Experimental results for electroencephalogram
	9.7 Discussion and conclusion
	9.8 Teaching assignments
	References
10 Mobile Partogram—m-Health technology in the promotion of parturient’s health in the delivery room
	10.1 Introduction
	10.2 The Mobile Partogram conception—m-Health technology in parturient care in the delivery room
	10.3 Participatory user-centered interaction design to support and understand the conception of partograma mobile
	10.4 Identifying needs and defining requirements
		10.4.1 Design of alternatives
	10.5 Building an interactive version (high-fidelity prototype)
	10.6 Evaluation (usability)
	10.7 Final considerations
	10.8 Teaching assignments
	References
11 Artificial intelligence–assisted detection of diabetic retinopathy on digital fundus images: concepts and applications i...
	11.1 Introduction
	11.2 Diabetic retinopathy in the National Health Service
	11.3 Predictive analytics in diabetic retinopathy screening
		11.3.1 Big data in the context of diabetic retinopathy screening
		11.3.2 Predictive analytics in diagnostic retina screening
		11.3.3 Evaluation and performance measures
	11.4 Implementation in a smart healthcare setting
		11.4.1 Upskilling the workforce
		11.4.2 Multimodal imaging in diabetic retinopathy: integrating optical coherent tomography
	11.5 Challenges
		11.5.1 Adoption and clinical governance
		11.5.2 Ethical and legal compliance
	11.6 Conclusion
	References
12 Virtual reality and sensors for the next generation medical systems
	12.1 Introduction
	12.2 Related work
	12.3 The proposed methodology
		12.3.1 Postural analysis stage
		12.3.2 Virtual modeling stage
		12.3.3 Self-assessment stage
		12.3.4 Analysis and presentation stage
	12.4 Experimental results
	12.5 Conclusions and future work
	12.6 Teaching assignments
	Acknowledgments
	References
13 Portable smart healthcare solution to eye examination for diabetic retinopathy detection at an earlier stage
	13.1 Introduction
	13.2 Fundus eye images: the fundus photography and its acquisition
	13.3 Fundus eye imaging and problems
	13.4 Smartphone fundus cameras in the market
		13.4.1 Volk iNview
		13.4.2 Peek vision
		13.4.3 D-EYE smartphone-based retinal imaging system
		13.4.4 ODocs eye care
	13.5 What is the problem?
	13.6 Impact of the problem
	13.7 Proposed solution
	13.8 Methodology and validation
	13.9 Popular ridge detectors for vessel segmentation
	13.10 Proposed method
	13.11 Experimental results
	13.12 Conclusion and future work
	13.13 Teaching assignments
	References
	Further reading
14 Improved nodule detection in chest X-rays using principal component analysis filters
	14.1 Introduction
	14.2 Looking at rib structure from signal processing point-of-view
	14.3 Data acquisition
	14.4 System design
		14.4.1 Local normalization
		14.4.2 Multiscale nodule detection
		14.4.3 Detection of nodules in discrete X-ray images
	14.5 Experiment
	14.6 Results
	14.7 Implication of automated lung nodules detection for future generation medical systems
	14.8 Discussion and conclusion
	14.9 Teaching assignments
	References
	Further reading
15 Characterizing internet of medical things/personal area networks landscape
	15.1 Introduction
		15.1.1 Internet of medical things and health informatics
		15.1.2 Personal area networks
	15.2 Architectural landscape
		15.2.1 Physical components
			15.2.1.1 Physical components
		15.2.2 Network component
			15.2.2.1 Bluetooth
				15.2.2.1.1 Protocol stack
				15.2.2.1.2 Pico and scatter networks
			15.2.2.2 Low-rate WPAN
			15.2.2.3 High-rate WPAN
			15.2.2.4 Body area networks
	15.3 Prevalent internet of medical things applications
		15.3.1 Internet of medical things services and applications
		15.3.2 Internet of medical things companies leading the way
	15.4 Conclusions and future directions
		15.4.1 Future research directions
		15.4.2 Recommended assignments
	References
Section D: Social Issues and policy making for smart healthcare
16 Threats to patients’ privacy in smart healthcare environment
	16.1 Introduction
	16.2 Definitions
	16.3 Legislation and policy
		16.3.1 Privacy rule in Health Insurance and Portability Accountability Act
		16.3.2 Federal Information Security Management Act of 2002
		16.3.3 Cyber Enhancement Act 2014
		16.3.4 NIST Cyber Security Framework
	16.4 Typical smart healthcare architecture
		16.4.1 Network layer
			16.4.1.1 Local Area Network
			16.4.1.2 Personal Area Network
			16.4.1.3 Wide Area Network
			16.4.1.4 Public Key Infrastructure
		16.4.2 Technology layer
		16.4.3 Applications layer
	16.5 Typical security threats
		16.5.1 Attacks’ classification
			16.5.1.1 Social engineering attacks
			16.5.1.2 Insider threats
			16.5.1.3 Denial of Service
			16.5.1.4 Viruses, trojans, and worms
			16.5.1.5 Typical hacking process
	16.6 Conclusion
		16.6.1 Future research directions
		16.6.2 Teaching assignments
	References
	Further reading
17 Policy implications for smart healthcare: the international collaboration dimension
	17.1 Introduction
	17.2 The smart healthcare utilization framework
	17.3 International collaboration for resilient smart healthcare
	References
	Further reading
Index
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