Green Computing and Predictive Analytics for Healthcare

Green Computing and Predictive Analytics for Healthcare

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Bibliographic Details
Main Author: Banerjee, Sourav
Other Authors: Chakraborty, Chinmay, Dasgupta, Kousik
Format: eBook
Language:English
Published: Milton : CRC Press LLC, 2020.
Subjects:
Medical care > Data processing.
Computer systems > Energy conservation.
Online Access:Click for online access
Table of Contents:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • Preface
  • About the Editors
  • List of Contributors
  • Chapter 1 Healthcare Data Monitoring under Internet of Things
  • 1.1 Introduction
  • 1.1.1 Healthcare Data
  • Efficient Storage of Big Data
  • 1.2 Digitization of Healthcare-Oriented Big Data
  • 1.3 Healthcare
  • IoT and Mobile Health
  • 1.4 Management of Big Data
  • 1.4.1 Electronic Medical Record (EMR) or Electronic Health Record (EHR)
  • 1.4.2 Healthcare Analytics
  • 1.5 Medical Data Analysis and Disease Predictions through ML
  • 1.6 Applications of Big Data in the Medical Field
  • 1.7 Analytics of Medical Data in the Mercantile Platform
  • 1.8 Related Work
  • 1.9 Challenges and Constraints Related to Healthcare-Based Big Data Concepts (Including Privacy and Security Issue)
  • 1.10 Conclusion and Future Trends
  • References
  • Chapter 2 A Framework for Emergency Remote Care and Monitoring Using Internet of Things
  • 2.1 Introduction
  • 2.2 The IoT Architecture and Applications
  • 2.2.1 Stage 1 (Sensors/Actuators)
  • 2.2.2 Stage 2 (Data Acquisition Systems)
  • 2.2.3 Stage 3 (Edge Analytics)
  • 2.2.4 Stage 4 (Cloud Analytics)
  • 2.3 Literature Survey
  • 2.4 A Proposed Framework for Emergency Remote Care and Monitoring Using Internet of Things
  • 2.4.1 Parameters for Prediction
  • 2.5 Proposed Work
  • 2.6 Results and Discussion
  • 2.7 Conclusion and Future Work
  • References
  • Chapter 3 Big Data Analytics and K-Means Clustering
  • 3.1 Introduction
  • 3.2 Big Data
  • 3.3 Predictive Analytics
  • 3.4 Predictive Modeling
  • 3.5 MapReduce Abstraction
  • 3.6 Resilient Distributed Datasets (RDDs)
  • 3.7 Computational Phenotyping
  • 3.8 Clustering
  • 3.9 Medicinal Oncology
  • 3.10 Dimensionality Reduction
  • 3.11 Patient Similarity
  • 3.12 Distance Metric Learning
  • 3.13 Graph-Based Similarity Learning
  • 3.14 Clustering Challenges of Big Data
  • 3.15 Algorithms for Large Datasets in Clustering
  • 3.16 Privacy and Security
  • 3.17 Various Approaches for Predictive Analytics
  • 3.18 Why Predictive Analytics and Big Data for Electronic Health Records?
  • 3.19 K-Means Clustering for Analysis of EHR
  • 3.20 K-Means for Very Large-Scale Dataset
  • 3.20.1 Tools and Applications in the Healthcare System
  • 3.20.2 Application of Big Data in Healthcare
  • 3.20.3 K-Means Clustering
  • 3.21 Partitioning Around Medoids (PAM)
  • 3.22 Hierarchical
  • 3.23 Density-Based Spatial Bunching of Applications with Noise (DBSCAN)
  • 3.24 Compatibility Issues
  • 3.25 Different Solutions, Supplementary Tasks?
  • 3.26 Priorities Engagement toward Analytics
  • 3.27 Paid, Free or Open Source Vendors?
  • 3.28 Data Clustering Strategy
  • 3.29 The Brilliant Future of Big Data in Healthcare
  • 3.30 Fueling the Big Data Healthcare Revolution
  • 3.31 Conclusion
  • References

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