Innovation in Health Informatics: A Smart Healthcare Primer

$19.99

Download Innovation in Health Informatics: A Smart Healthcare Primer written by Miltiadis D. Lytras, Akila Sarirete in PDF format. This book is under the category Health and bearing the isbn/isbn13 number 0128190434; 0128190442/9780128190432/ 9780128190449. You may reffer the table below for additional details of the book.

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Specifications

book-author

Miltiadis D. Lytras, Akila Sarirete

publisher

Academic Press

file-type

PDF

pages

356 pages

language

English

asin

B081KVBH6P

isbn10

0128190434; 0128190442

isbn13

9780128190432/ 9780128190449


Book Description

Innovation in Health Informatics: A Smart Healthcare Primer; (PDF) explains how the most recent advances in data and communication applied sciences have paved the way in which for brand new discoveries in healthcare. The ebook showcases present and potential purposes in a context outlined by an crucial to supply affected person-centered; environment friendly; and sustainable healthcare programs. Topics mentioned include massive information; synthetic intelligence; machine studying; medical information analytics; digital and augmented actuality; Internet of Things; 5G and sensors; nanotechnologies; and biotechnologies. Moreover; there’s a dialogue on social points and policymaking for the implementation of sensible healthcare.

This ebook is a useful useful resource for graduate and undergraduate college students; practitioners; researchers; information scientists; and clinicians who’re in the way to discover the intersections between well being informatics and bioinformatics.

  • Provides a case research pushed method; with references to actual-world purposes and programs
  • Discusses matters with a analysis-oriented method that intends to advertise analysis expertise and competencies of readers
  • Presents a holistic dialogue on the brand new panorama of medical applied sciences; together with massive information; analytics; synthetic intelligence; digital and augmented actuality; 5g and sensors; Internet of Things; machine studying; nanotechnologies; and biotechnologies

NOTE: This sale solely consists of the ebook Innovation in Health Informatics: A Smart Healthcare Primer in PDF. No access codes are included

 

Additional information

book-author

Miltiadis D. Lytras, Akila Sarirete

publisher

Academic Press

file-type

PDF

pages

356 pages

language

English

asin

B081KVBH6P

isbn10

0128190434; 0128190442

isbn13

9780128190432/ 9780128190449

Table of contents


Table of contents :
Cover
Innovation in Health Informatics: A Smart Healthcare Primer
Copyright
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
Back Cover

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