Prashant Natarajan, John C Frenzel, Detlev H Smaltz
Demystifying Big Data and Machine Learning for Healthcare
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Prashant Natarajan, John C Frenzel, Detlev H Smaltz
Demystifying Big Data and Machine Learning for Healthcare
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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
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 210
- Erscheinungstermin: 30. Juni 2021
- Englisch
- Abmessung: 254mm x 178mm x 11mm
- Gewicht: 372g
- ISBN-13: 9781032097169
- ISBN-10: 1032097167
- Artikelnr.: 62150769
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 210
- Erscheinungstermin: 30. Juni 2021
- Englisch
- Abmessung: 254mm x 178mm x 11mm
- Gewicht: 372g
- ISBN-13: 9781032097169
- ISBN-10: 1032097167
- Artikelnr.: 62150769
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
* 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
* 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