Demystifying Big Data and Machine Learning for Healthcare

Healthcare transformation requires us to continually look at new and better ways to manage insights - both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization's day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: - Develop skills needed to identify and demolish big-data myths - Become an expert in separating hype from reality - Understand the V's that matter in healthcare and why - Harmonize the 4 C's across little and big data - Choose data fi delity over data quality - Learn how to apply the NRF Framework - Master applied machine learning for healthcare - Conduct a guided tour of learning algorithms - Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz

Chapter 1: Introduction - What is big data and how is it similar/different from business intelligence or analytics - the basics? Analytics 1.0, 2.0, and 3.0 - Why big data needs machine learning - in brief Chapter 2: Healthcare and the Big Data V's - The case for big data - market analysis - vendors and applications - Introduction to the V's - When do we need to care about data quality? - What can you do with this data? Introduction to Types of analytics Chapter 3: Big Data - How to Get Started - Getting started within your Organization - Assessing your environment and organizational readiness - Understanding the data needed to support the use cases - Organizational structuring considerations for big data Chapter 4: Big Data - Challenges - Skills gap - The need for data governance - Understanding data quality and big data - The role of Master Data Management - The big brother challenge - Going beyond silos - how to integrate insights between big and small data Chapter 5: Best Practices - Debunking some common myths - Executive sponsorship need; what must an executive sponsor do to ensure a data driven culture? CAO or CDO - is there a need? What are the similarities & differences? - Is the DW dead with the advent of big data? What happens to my existing analytics? - Big data and the cloud, an introduction - Best Practices to ensure success Chapter 6: Machine Learning and Healthcare - the Big Data Connection - What is AI? What is ML? How are they related to data mining & data science? Can we demystify the terminology? - Real life examples from outside healthcare - Netflix, Amazon, Siri, etc - What does it mean for healthcare? Why should you care? State of the industry. - Inductive v Deductive v Other reasoning - an introduction and why should we care - Types of Machine Learning - what are learning algorithms? - Supervised, unsupervised, semi-supervised, reinforcement with some discussion. What is deep learning? - Popular algorithms and how they are used - Computational biomarkers, data charting, visualization - a discussion in context - Representative use cases in healthcare - Medical imaging ML & imaging biomarkers for Traumatic brain injury - UCSF - Population Health: ML for diabetes prediction - Cardiology predictive analytics - Stanford Chapter 7: Advanced Topics - Unstructured data & text analysis: NLP - The learning organization and knowledge management Chapter 8: Case Studies from healthcare organizations - MD-Anderson Cancer Center - Penn OMICS - CIAPM - - Ascension case study - Deloitte case study Appendix A. Big data technical glossary