Hjem

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