COMP06289 2020 Big Data in Health

General Details

Full Title
Big Data in Health
Transcript Title
Big Data in Health
Code
COMP06289
Attendance
N/A %
Subject Area
COMP - Computing
Department
HEAL - Health & Nutritional Sciences
Level
06 - NFQ Level 6
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2020 - Full Academic Year 2020-21
End Term
9999 - The End of Time
Author(s)
Padraig McGourty, Thomas Smyth, Richeal Burns, Dr. Sasirekha Palaniswamy Lecturer
Programme Membership
SG_SINFO_B07 202000 Bachelor of Science in Health and Medical Information Science SG_SINFO_C06 202000 Higher Certificate in Science in Health and Medical Information Science
Description

This module will introduce Big Data, it’s characteristics and address the key concepts underlying the Big Data in Health. The theoretical underpinnings of these concepts will be presented along with its application in healthcare. Key concepts such as knowledge discovery, precision medicine and improved health outcomes will be addressed. Big data analytics and tools will be reviewed along with relevant data analytics and techniques. Issues related to Big Data with respect to gathering, analysing, visualising and interpreting big data will be discussed. The importance of standards and interoperability, data governance, privacy and data protection will be addressed. This module will provide a hands-on practical experience to analyse Big Data using Cloud computing/R/Python.

Learning Outcomes

On completion of this module the learner will/should be able to;

1.

To gain knowledge and understanding of Big Data, its characteristics, sources of big data and data sharing whilst ensuring privacy and governance.

2.

To demonstrate knowledge of the principles underpinning the tools of Big data analysis in health related research.

3.

To gain understanding of standards for interoperability in Health - Data discovery, accessibility, reusable and interoperable

4.

To apply data analytic tools in Healthcare, dimensionality reduction, extracting and working with Big Data to gain knowledge and understanding of Big Data analytics.

Teaching and Learning Strategies

Teaching and learning for this module will be carried out through a combination of online lectures, computer based critical appraisal and online practical's. Blended learning approaches will be adapted consistent with digital learning paradigms. 

Online delivery of 1 lecture per week with self directed learning. Guidance provided on relevant areas for self directed learning.

Online delivery of 2 hour workshop weekly, where students will be directed to complete interactive type activities to enhance their study skills and knowledge.

Question and answer sessions provided in the live classroom.

A variety of methods of instruction such as discussion, group work, interactive exercises, use of online resources and/or use of audio/visual material will be provided. Core skills will be embedded into all modules to ensure all students have an equal opportunity to succeed. This may include academic writing, oral presentations, reading techniques or research abilities. Accessible materials will be provided to students, including slides, documents, audio/visual material and textbooks enabling students slow down speed up recordings etc in accordance with universal distance learning.

All module content will be based on the principles of UDL to ensure equitable access to content and learning.

Module Assessment Strategies

This module will be assessed by both a final exam (50%) and continuous assessment (50%)

Repeat Assessments

Repeat examination will follow a similar format as applicable

Indicative Syllabus

  • Definitions of Big Data, characteristics (3Vs - Volume, Velocity and Variety)

  • Data Sources - Explore sources of Big Data, their quality and safety, collection, storage and processing.

  • Open data and data sharing -, promote open use and sharing of Big Data in Health

  • How to utilise the strengths and exploit the opportunities of Big Data for Public Health without threatening privacy or safety of citizens. 

  • Data Analysis - Identify the potentials of Big Data analysis, improve analytical methods and facilitate the use of new and innovative analytical methods

  • Data analytics in healthcare - Dimensionality Reduction

  • Big data analytics - MapReduce, Hadoop, Spark

  • Cloud Computing - AWS, AZURE, (other cloud/online resources)

  • Data analytical tools - R, Python 

  • Privacy and data protection - Patients’ rights to privacy and confidentiality

  • Issues related to big data: challenges faced when working with Big Data, challenges in using population health data sources, Population segmentation

  • Obstacles - medical and clinical data is spread across many sources governed by different departments and sometimes different organisations. Integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other. 

  • Stakeholders’s roles and responsibilities

  • Standards for interoperability

  • Issues related to Big Data in Health, legal aspects and privacy regulation, existing regulatory frameworks (eg. data protection, informed consent, quality, safety and reliability), governance mechanisms (availability, usability, integrity and security) to ensure secure and fair access and use of Big Data for research in health

  • Ethical standards - consent, stakeholders involvement in the process of producing and implementing recommendations

  • Existing policy recommendations and guidelines for the development of a Big Data value chain. 

  • Examples of use of Big Data - Genomic data, UK Biobank, Hospital Episode Statistics (HES) - UK, E-Estonia - National Identity Scheme

  • Partnerships - Public-private partnerships and other partnerships to support and accelerate Big Data use in health practice.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
50 %
End of Semester / Year Formal Exam
50 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Big Data in Health - Project Continuous Assessment Project 50 % OnGoing 2,4
             
             

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Big Data in Health - Exam Final Exam Closed Book Exam 50 % End of Semester 1,3
             
             

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Online Big Data in Health 1 Weekly 1.00
Problem Based Learning Online Big Data in Health PBL 2 Weekly 2.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Required & Recommended Book List

Required Reading
2019-02-05 Machine Learning and AI for Healthcare Apress
ISBN 1484237986 ISBN-13 9781484237984

Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. Youll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. Youll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll Learn Gain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agents Who This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

Required Reading
2017-01-07 Health 4.0: How Virtualization and Big Data are Revolutionizing Healthcare Springer
ISBN 9783319476179 ISBN-13 3319476173

This book describes how the creation of new digital servicesthrough vertical and horizontal integration of data coming from sensors on top of existing legacy systemsthat has already had a major impact on industry is now extending to healthcare. The book describes the fourth industrial revolution (i.e. Health 4.0), which is based on virtualization and service aggregation. It shows how sensors, embedded systems, and cyber-physical systems are fundamentally changing the way industrial processes work, their business models, and how we consume, while also affecting the health and care domains. Chapters describe the technology behind the shift of point of care to point of need and away from hospitals and institutions; how care will be delivered virtually outside hospitals; that services will be tailored to individuals rather than being designed as statistical averages; that data analytics will be used to help patients to manage their chronic conditions with help of smart devices; and that pharmaceuticals will be interactive to help prevent adverse reactions. The topics presented will have an impact on a variety of healthcare stakeholders in a continuously global and hyper-connected world. Presents explanations of emerging topics as they relate to e-health, such as Industry 4.0, Precision Medicine, Mobile Health, 5G, Big Data, and Cyber-physical systems; Provides overviews of technologies in addition to possible application scenarios and market conditions; Features comprehensive demographic and statistic coverage of Health 4.0 presented in a graphical manner.

Required Reading
2019-10-15 Big Data Analytics in Healthcare Springer
ISBN 3030316718 ISBN-13 9783030316716

This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms.

Required Reading
2017-09-18 Big Data in Healthcare Springer
ISBN 9783319629902 ISBN-13 3319629905

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.

Required Reading
2018-12-21 Fundamentals of Clinical Data Science Springer
ISBN 9783319997131 ISBN-13 3319997130

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The books promise is no math, no codeand will explain the topics in a style that is optimized for a healthcare audience.

Module Resources

Journal Resources

Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients -  David W. Bates, Suchi Saria, Lucila Ohno-Machado, Anand Shah, and Gabriel Escobar

Other Resources

Python

R