Big Data in Healthcare Extracting Knowledge from Point of Care Machines 1st edition by Pouria Amirian 3319629883 9783319629889

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Product details:

ISBN 10: 3319629883

ISBN 13: 9783319629889

Author: Pouria Amirian

This book reviews a number of issues including: Why data generated from POC machines are considered as Big Data. What are the challenges in storing, managing, extracting knowledge from data from POC devices? Why is it inefficient to use traditional data analysis with big data? What are the solutions for the mentioned issues and challenges? What type of analytics skills are required in health care? What big data technologies and tools can be used efficiently with data generated from POC devices? This book shows how it is feasible to store vast numbers of anonymous data and ask highly specific questions that can be performed in real-time to give precise and meaningful evidence to guide public health policy.

Big Data in Healthcare Extracting Knowledge from Point of Care Machines 1st Table of contents:

1 Introduction—Improving Healthcare with Big Data

1.1 Introduction

1.2 Big Data and Health

1.3 Big Data and Health in Low- and Middle-Income Countries

1.3.1 Analytical Challenges

1.3.2 Ethical Challenges

1.3.2.1 Informed Consent

1.3.2.2 Privacy

1.3.2.3 Ownership

1.3.2.4 Epistemology and Objectivity

1.3.2.5 Big Data ‘Divides’

1.4 Conclusion and Structure of the Book

References

2 Data Science and Analytics

2.1 What Is Data Science?

2.2 Methods in Data Science

2.2.1 Supervised and Unsupervised Learning

2.2.2 Data Science Analytical Tasks

2.3 Data Science, Analytics, Statistics, Business Intelligence and Data Mining

2.3.1 Data Science and Analytics

2.3.2 Statistics, Statistical Learning and Data Science

2.3.3 Data Science and Business Intelligence

2.4 Data Science Process

2.4.1 CRISP-DM

2.4.2 Domain Knowledge and Business Understanding

2.4.3 Data Understanding and Preparation

2.4.4 Building Models and Evaluation Metrics

2.4.5 Model Deployment

2.5 Data Science Tools

2.6 Summary

References

3 Big Data and Big Data Technologies

3.1 What Is Big Data?

3.2 Data Dimension of Big Data

3.2.1 Volume

3.2.2 Velocity

3.2.3 Variety

3.2.4 Other Vs of Big Datasets

3.3 Structured, Unstructured and Semi-structured Data

3.3.1 Internet of Things and Machine-Generated Data

3.3.2 Highly Connected Data

3.4 Big Data Technologies

3.4.1 Building Blocks of Hadoop: HDFS and MapReduce

3.4.2 Distributed Processing with MapReduce

3.4.3 HDFS and MapReduce

3.4.4 Hadoop Ecosystem: First Generation

3.4.5 Hadoop Ecosystem Second Generation

3.5 Splunk: A Commercial Big Data Technology

3.6 Big Data Pipeline: Lambda and Kappa Architectures

3.6.1 Lambda Architecture

3.6.2 Kappa Architecture

3.7 Big Data Tools and Technologies

References

4 Big Data Analytics for Extracting Disease Surveillance Information: An Untapped Opportunity

4.1 Introduction

4.2 The Importance of POC

4.3 Technical Requirements of POC

4.4 Data Generated by POC and Accessibility Issue

4.5 Proposed Solution

4.5.1 Common Data Structure of the Proposed Solution

4.5.2 Data Analytics in the Proposed Solution

4.6 Big Data Architecture of the Proposed Solution

4.7 Benefits of the Implemented System

4.8 The Implemented Data Analytics and Dashboards

4.9 Conclusions and Future Work

References

5 #Ebola and Twitter. What Insights Can Global Health Draw from Social Media?

5.1 Introduction

5.2 Ebola Virus Disease and Media Coverage

5.3 How Can We Study Social Media Data?

5.4 Insights from the Ebola Twitter Dataset

5.5 Conclusion

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