Handbook of Data Science Approaches for Biomedical Engineering

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Handbook of Data Science Approaches for Biomedical Engineering
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Contents
Contributors
1. Analysis of the role and scope of big data analytics with IoT in health care domain
	1. Introduction
	2. Sources of health care data
		2.1 Electronic health records (EHR)
		2.2 Clinical text mining
		2.3 Medical imaging data
		2.4 Genomic data
		2.5 Behavioral data
	3. Tools and data analytics interfaces in medical and health care system
		3.1 Advanced data visualization (ADV)
		3.2 Presto
		3.3 Hive
		3.4 Vertica
		3.5 Key performance indicators (KPI)
		3.6 Online analytics processing (OLAP)
		3.7 Online transaction processing (OLTP)
		3.8 The Hadoop distributed file system (HDFS)
		3.9 Casandra file system (CFS)
		3.10 Map reduce system
		3.11 Complex event processing (CEP)
		3.12 Text mining
		3.13 Cloud computing
		3.14 Mahout
		3.15 JAQL
		3.16 AVRO
	4. Health care with big data challenges
		4.1 Issues related to policy and fiscal factors
		4.2 Issues related to technology
	5. IoT defined
	6. IoT for health care
	7. Challenges for IoT in health care
	8. Evolution of big data in medical IoT
	9. Advantages
	10. Literature survey
	11. Implementation of a real-time big data analytics of IoT-based health care monitoring system
		11.1 Components and methods
		11.2 Results and discussion
	12. Conclusion
	References
2. Automated human cortical bone Haversian canal histomorphometric comparison system
	1. Introduction
	2. Sample collection
	3. Sample preparation
		3.1 Specimen defatting
		3.2 Sectioning of bone specimen
		3.3 Specimen grinding and polishing
		3.4 Glass slide mounting
	4. Difficulties in sample preparation
		4.1 Trapped air bubbles in the glass sample
		4.2 Thick bone slice
		4.3 Uneven thickness
		4.4 Broken section
		4.5 Dirty specimen
		4.6 Fragile bone specimen
	5. Image acquisition
	6. Microstructural parameter selection
		6.1 Haversian canal number (hcn)
		6.2 Mean Haversian canal area (hcm)
		6.3 Total Haversian canal area
		6.4 Mean Haversian canal radius
		6.5 Mean Haversian canal perimeter
		6.6 Percentage area covered by Haversian canal (hcpar)
	7. Inclusion and exclusion criteria
	8. Statistical tests
	9. Automated comparison system
		9.1 Comparison test selection
	10. Automated system design
	11. Sex comparison without age groups
		11.1 Sex comparison hcm
		11.2 Sex comparison hca
		11.3 Sex comparison hcr
		11.4 Sex comparison hcp
		11.5 Sex comparison hcn
		11.6 Sex comparison hcpar
		11.7 Sex comparison discussion
	12. Race comparison without age groups
		12.1 Race comparison hcm
		12.2 Race comparison hca
		12.3 Race comparison hcr
		12.4 Race comparison hcp
		12.5 Race comparison hcn
		12.6 Race comparison hcpar
		12.7 Race comparison discussion
	13. Conclusion
	References
3. Biomedical instrument and automation: automatic instrumentation in biomedical engineering
	1. Introduction
	2. Biomedical instrumentation
	3. Automation in the field of biomedical instrumentation
		3.1 Automation in medical instruments
	4. Automation in telerobotic surgeries
		4.1 Origin of surgical robots
	5. Types of robotic surgeries
		5.1 Type-1 supervisory—controlled surgery systems
		5.2 Type-2 shared-control robotic surgery systems
		5.3 Type-3 tele surgical robotic surgery system
			5.3.1 da Vinci Surgical System
			5.3.2 ZEUS robotic surgical system (ZRSS)
			5.3.3 Automated endoscopic system for optimal positioning (AESOP) robotic surgical system
	6. Applications
		6.1 PROS and CONS of surgical robots
			6.1.1 PROS
			6.1.2 CONS
		6.2 The future of surgical robots
	7. Automatic wireless sensor networking in biomedical instrumentation
	8. Biomedical applications of wireless sensor networking
		8.1 IEEE 802.15.4
		8.2 Open system interconnect layered architecture
	9. Network topology
	10. Bluetooth communication
		10.1 Bluetooth modules used for biomedical applications
	11. Sensing technologies
		11.1 Invasive biosensors for WSN
		11.2 Noninvasive bio sensors for WSN
		11.3 Respiration rate sensor
		11.4 RF and antenna communication
	12. Selecting RF transceivers
		12.1 Specifications
		12.2 Safety issues
	13. Recent advancements and applications in biomedical instrumentation
		13.1 Biomedical instrumentation in medical imaging
		13.2 Biomedical instrumentation in medical devices
		13.3 Biomedical instrumentation in tissue engineering
		13.4 Biomedical instrumentation in implants and bionics
		13.5 Biomedical instrumentation in clinical engineering
		13.6 Biomedical instrumentation in neural engineering
		13.7 Biomedical instrumentation in rehabilitation engineering
		13.8 Applications of automation in biomedical instrumentation
	14. Conclusion
	References
4. Performance improvement in contemporary health care using IoT allied with big data
	1. Introduction
		1.1 Outline of IoT and big data
		1.2 Technology modernization and quality as a challenge in health care systems
		1.3 Availability of health care information in social media
		1.4 Smart applications related to health care systems using IoT
		1.5 ICT and big data in health care development
		1.6 Cyber physical cloud computing and health care approaches
		1.7 Big data analytic methods in health care
		1.8 Decision making tools and logic implementation in big data
		1.9 Quality assessment model in big data
		1.10 Health care monitoring frameworks
	2. Conclusion
	References
5. Emerging trends in IoT and big data analytics for biomedical and health care technologies
	1. Introduction
	2. Big data workflow for biomedical image analysis
	3. Role of artificial intelligence and robotics in telemedicine
		3.1 Robotics in health care
		3.2 History of robotics
		3.3 Tele-surgery/remote surgery
		3.4 Applications
		3.5 Artificial intelligence (AI)
		3.6 Internet of Robotic Things (IoRT)
	4. Wearable devices and IoT
		4.1 Classification and categories of wearable devices
		4.2 Communication modes of wearable devices in IoT
		4.3 Very short distance
		4.4 Short distance
		4.5 Long distance communication
		4.6 Working principles of wearable devices in IoT
		4.7 Applications of wearable devices in IoT
		4.8 Research challenges and open issues
	5. Biotechnological advances
		5.1 Neuroscience and brain research
		5.2 Gene therapy
		5.3 Big data enhancing stem cell research and tissue engineering
		5.4 Big data of nanotechnology to nanomedicine
		5.5 New drug discovery and drug delivery systems
	6. Conclusion
	References
6. Recent advances on big data analysis for malaria prediction and various diagnosis methodologies
	1. Introduction
	2. Disease prediction model based on big data analysis
	3. Diagnosis techniques
		3.1 Clinical diagnosis
		3.2 Manual microscopic examination of blood smear
		3.3 Quantitative buffy coat (QBC)
		3.4 Rapid diagnostic test (RDT)
		3.5 Computerized diagnosis
			3.5.1 Database collection setup
			3.5.2 Preprocessing of blood smear image
			3.5.3 Segmentation
				3.5.3.1 Erythrocyte segmentation
				3.5.3.2 Infected erythrocyte and parasite segmentation
			3.5.4 Microscopic feature extraction
			3.5.5 Feature selection
			3.5.6 Malaria infection identification
				3.5.6.1 k-Nearest neighbor
				3.5.6.2 Neural network
				3.5.6.3 Support vector machine
				3.5.6.4 Naïve Bayes
				3.5.6.5 Multivariate regression
				3.5.6.6 Ada-Boost
				3.5.6.7 Euclidean distance classifier
				3.5.6.8 Hybrid classifier
			3.5.7 Computer-aided malaria diagnosis
	4. Discussion
	5. Conclusion
	Acknowledgments
	References
Chapter 7 - Semantic interoperability in IoT and big data for health care: a collaborative approach
	1. Introduction
	2. State of the art
		2.1 Internet of Things (IoT)
		2.2 Cloud computing
		2.3 U-health care system
			2.3.1 Body Area Network (BAN)
				2.3.1.1 Wireless Body Area Network (WBAN)
				2.3.1.2 Personal monitoring devices (PMD)
			2.3.2 Intelligent Medical Server (IMS)
			2.3.3 Hospital system
	3. Semantic interoperability
		3.1 Ontologies and Standards
		3.2 Mapping Technologies for Data Models
		3.3 Data integration and exchange systems
		3.4 Semantic annotations
	4. Semantic interoperability in IoT health care
		4.1 Adding semantic annotations to the IoT health care data
		4.2 Experiments and results
	5. SI in big data health care
		5.1 Adding semantic annotations to the big data health care data
		5.2 Experiments and results
	6. Conclusion and future work
	References
8. Why big data, and what it is: basics to advanced big data journey for the medical industry
	1. Introduction
	2. Why big data?
		2.1 Application to medical industry
			2.1.1 Big data in a medical domain
			2.1.2 Electronic health records
			2.1.3 Real-time alerts
			2.1.4 Evidence-based medicine
			2.1.5 Hospital readmissions
			2.1.6 Fraud detection
	3. Health care and the four Vs of big data
	4. An architecture of large-scale platform to develop a predictive model
		4.1 Types of big data
			4.1.1 Primitive big data
			4.1.2 Nonprimitive big data analytics
		4.2 Platform to big data
			4.2.1 The Hadoop Distributed File System (HDFS)
			4.2.2 Map Reduce
			4.2.3 PIG and PIGLatin
			4.2.4 Jaql
			4.2.5 HBase
			4.2.6 Cassandra
			4.2.7 Avro
			4.2.8 Hive
	5. The model through big data analytics
		5.1 An architecture of large-scale platform to develop a predictive model
			5.1.1 Map Reduce (Map and Reduce)
		5.2 Functional network algorithm
			5.2.1 Functional network (n/w) can be learned by the use of one of the optimization techniques
				5.2.1.1 Model selection
	6. Impact of big data
		6.1 Examples to complex biomedical information
			6.1.1 Dell health care solutions
			6.1.2 IBM health care and life sciences
			6.1.3 Intel health care
			6.1.4 Amazon web services
			6.1.5 GE health care life science
			6.1.6 Oracle life sciences
			6.1.7 Cisco health care solutions
		6.2 Personalized medicines
	7. Ethical issues
		7.1 Ethical themes
			7.1.1 Consent
				7.1.1.1 Informed Consent
				7.1.1.2 Single-instance consent
				7.1.1.3 “Broad” and “Blanket” consent mechanisms
				7.1.1.4 Tired consent
			7.1.2 Data protection
			7.1.3 Privacy
			7.1.4 Ownership
			7.1.5 Epistemology
			7.1.6 Objectivity
	8. Conclusion
	References
	Further reading
9. Semisupervised fuzzy clustering methods for X-ray image segmentation
	1. Introduction
	Part 1: Theory background
	1.1 Image segmentation problem
	1.2 Data clustering
	1.3 Fuzzy clustering
	1.4 Semisupervised fuzzy clustering
		1.4.1 Semisupervised entropy regularized fuzzy clustering-eSFCM
	Part 2: The combination of eSFCM and OTSU in image segmentation
	2.1 The general diagram of the integration between the eSFCM and OTSU
	2.2 OTSU threshold algorithm in image processing
	Part 3: Semisupervised fuzzy clustering with spatial feature
	3.1 The general framework
	3.2 Determining suitable additional information
	3.3 The semisupervised fuzzy clustering algorithm (SSFC-SC)
		3.3.1 Dental image segmentation model
		3.3.2 Solving the segmentation problem using Lagrange multiplier
	3.4 Fuzzy satisficing method and semisupervised clustering method in segmentation problem (SSFC-FS )
	3.5 The properties and consequences from solution analysis
	3.6 The advantages of the proposed algorithms
	Part 4: Defining the suitable additional information for SSFC-FS algorithm
	4.1 The framework of the SSFC-FSAI method
	4.2 The set of additional information functions
	4.3 Defining an appropriate additional information
	4.4 Advantages of the new algorithm
	Part 5: The results of implementations and applications
	5.1 Dental X-ray image dataset
		5.1.1 Data description
		5.1.2 Defining features
		5.1.3 The validity indices and evaluation criteria
	5.2 The performance among segmentation methods
		5.2.1 Experiments in dental X-ray image dataset
		5.2.2 The results of clustering algorithms according to changing of parameters
	2. Conclusions
	Acknowledgments
	References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Z
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