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