Demystifying Big Data and Machine Learning for Healthcare

ISBN-13: 9781032097169 / Angielski / Miękka / 2021 / 210 str.

John C. Frenzel; Detlev H. Smaltz

Opis

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

Spis treści

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

O autorze

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