book

The Internet of Medical Things (IoMT)

by R. J. Hemalatha, D. Akila, D. Balaganesh, Anand Paul

March 2022

Intermediate to advanced

336 pages

8h 27m

English

Wiley-Scrivener

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1.1 Introduction1.2 Methodology1.3 Results and Discussion1.4 ConclusionReferences
2.1 Introduction2.2 Access Control–Based Security2.3 System Model2.4 Data Classification2.5 Related Work2.6 ConclusionReferences
3.1 Introduction3.2 Existing Technology and Its Review3.3 Research Design3.4 Results3.5 Discussion3.6 ConclusionReferences
4.1 Introduction4.2 Classes of Brain Tumors4.3 Literature Survey4.4 Methodology4.5 ConclusionReferences
5.1 Introduction5.2 Related Works5.3 Materials and Methods5.4 Results and Discussion5.5 ConclusionReferences
6.1 Introduction6.2 Deep Learning Models6.3 Interpretation of Deep Learning With Biomedical Data6.4 ConclusionReferences

7.1 Introduction7.2 Traditional Paper Method7.3 IoMT7.4 Telemedicine and IoMT7.5 Cyber Security7.6 Materials and Methods7.7 Literature Review7.8 Applications of Electronic Health Records7.9 Results and Discussion7.10 Challenges Ahead7.11 ConclusionReferences
8.1 Introduction8.2 Preferences of the Internet of Things8.3 IoMT Progress in COVID-19 Situations: Presentation8.4 Major Applications of IoMTReferences
9.1 Introduction9.2 IoMT Architecture and Platform9.3 Types of Protocols9.4 Testing Process in IoMT9.5 Issues and Challenges9.6 ConclusionReferences
10.1 Introduction10.2 Proposed System Framework10.3 Experimental Evaluation10.4 Performance Evaluation10.5 ConclusionReferences
11.1 Introduction11.2 Materials and Methods11.3 Results and Discussion11.4 Summary11.5 ConclusionReferences
12.1 Introduction12.2 IoMT in Developing Wearable Health Surveillance System12.3 Vital Parameters That Can Be Monitored Using Wearable Devices12.4 Challenges Faced in Customizing Wearable Devices12.5 ConclusionReferences
13.1 Introduction13.2 Big Data and IoT in the Healthcare Industry13.3 Biomedical Big Data Types13.4 Biomedical Data Acquisition Using IoT13.5 Biomedical Data Management Using IoT13.6 Impact of Big Data and IoMT in Healthcare13.7 Discussions and ConclusionsReferences
14.1 Introduction14.2 Blockchain14.3 Blockchain as a Decentralized Security Framework14.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science14.5 Literature Review: Medical Data Security in Cloud Storage14.6 ConclusionReferences
15.1 Introduction15.2 Ancient Medical Record, 1600 BC15.3 Greek Medical Record15.4 Islamic Medical Record15.5 European Civilization15.6 Swedish Health Record System15.7 French and German Contributions15.8 American Descriptions15.9 Beginning of Electronic Health Recording15.10 ConclusionReferences

Content preview from The Internet of Medical Things (IoMT)

13Future of Healthcare: Biomedical Big Data Analysis and IoMT

Tamiziniyan G.¹^* and Keerthana A.²

¹GRT Institute of Engineering and Technology, Tiruttani, Tamil Nadu, India

²Vels Institute of Science, Technology & Advanced Studies (VISTAS), Velan Nagar, P.V. Vaithiyalingam Road, Pallavaram, Chennai, Tamil Nadu, India

Abstract

Biomedical big data analysis is an advanced technique exploring a plethora of datasets for extracting useful diagnostic and therapeutic information. It assists biomedical researchers to develop new algorithms and prediction models. Advancements in big data will improve the quality of diagnosis and prophylaxis. Leveraging big data analysis will reduce the challenges in healthcare ecosystem. Integration of datasets can support the healthcare providers for better patient outcomes. Big data analysis has a huge impact in personalized medicine. Development of different biomedical data repositories for medical images, biosignals and biochemistry will strengthen medical data analysis. Data collection and storage over the cloud is getting attention as the usage of wearable sensor is becoming well accepted. Cloud storage can share the information across different healthcare systems and impart possibilities for big data analytics. Internet of Things (IoT) can be used in biomedical and health monitoring applications which comprises of various biosensors and medical devices that are connected to the network. This will produce enormous data for better diagnostics and ...