The promise of AI-driven precision medicine creates confusion around what problems can be solved with data. After a lengthy career as an expert consultant helping industry leaders hone the scope of their big data projects, solutions designer Prashant Natarajan focuses our attention to what precision medicine can do by first defining what it actually is (and isn’t). Prashant discusses the definition of precision medicine as he defined in Multidisciplinary Approach to Head and Neck Cancer
Show Notes:
3:25 Details on Prashant’s current work, “Demystifying AI for the Enterprise”
4:13 What is Precision Medicine?
5:43 Important concepts in Precision Medicine.
11:20 Why Social Determinants of Health matter more than you think.
13:40 What is NOT Precision Medicine?
16:50 How Precision Medicine effects the healthcare analytics team.
21:25 What data does Precision Medicine use to develop the individualized risk profile?
24:00 Execution of feeding data into your models.
25:45 A list of data sources you should be collecting.
28:25 The goals of big data management and tools for health IT to accomplish them.
32:20 Training and skills for the CIO’s team.
35:00 All levels of providers will be affected and they will all need training too.
40:30 Practical applications for new machine learning trends and advances in Precision Medicine.
42:20 The best way to get feedback and how to know if interventions are working (i.e. feedback loops).
47:30 Prashant Natarajan’s new role at H2O.ai
51:10 Health AI uses and industry segments.
About Prashant Natarajan
Prashant Natarajan is Senior Director & Product Maker at H2O.ai where he is responsible for AI solutions for industry verticals: health, life sciences, & government. He is passionate about helping healthcare organizations maximize their technology investments to improve personal wellness, care access & affordability, clinician satisfaction, and health policy. Prior to joining the H2O.ai team, Prashant contributed to award-winning roles as a product manager, domain specialist, and expert consultant at Oracle, McKesson, Siemens (now Cerner), Healthways, and eCredit.com.
His current areas of focus are health insurance, drug discovery & safety, precision medicine, population health, and opioid crisis. He is a recognized executive leader, industry/domain expert, and hands-on solutions designer. Prashant received his master’s degree in technical communications & linguistics from Auburn University. He has an undergraduate degree in Chemical Engineering from Manipal University.
Prashant is a best-selling author and contributor to books on business intelligence, big data analytics, applied machine learning, and head & neck cancer. He is currently writing “Demystifying AI for the Enterprise” (2018), which separates reality from hype using validated use cases and best practices across multiple industry verticals.
Prashant is Industry Advisor for the California Initiative to Advance Precision Medicine (CIAPM) project at San Francisco VA. He is on the Board of Advisors at Council for Affordable Health Coverage, Washington DC. He enjoys teaching Applied Deep/Machine Learning & Artificial Intelligence as Co-Faculty Instructor at Stanford University, Palo Alto. Prashant also serves on Rutgers University’s Big Data Advisory Board. He lives in Livermore, CA, with his wife, Vishnu; 6-year old, Shivani, baby Neel, and his Australian Cattle Dog, Simba.
LinkedIn: https://www.linkedin.com/in/natarpr/
Twitter: @BigDataCXO
Listen to the interview:
Or, listen right here:
And checkout our last show with Prashant as well:
HIMSS18: Prashant Natarajan | Demystifying Big Data and Machine Learning for Healthcare
About Demystifying Big Data and Machine Learning for Healthcare
I find this book’s practical approach to enabling big data and machine learning through proper data management to be very refreshing. It lays out many of the data quality and analytics concepts that it took me 15+ years to learn the hard way. I strongly recommend this book to anyone working on data and analytics problems in healthcare.
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 fidelity 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.
Find it on:
Amazon: Demystifying Big Data and Machine Learning for Healthcare (Himss Book)
About H2O.ai
From their about page:
H2O.ai is the leader in AI with its visionary open source platform, H2O. Its mission is to democratize AI for all. H2O.ai is transforming the use of AI within all software with its category-creating visionary open source machine learning movement. More than 12,600 companies use open-source H2O in mission-critical use cases for Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing. H2O.ai recently launched Driverless AI that uses AI to do AI in order to provide an easier, faster and cheaper means of implementing data science.
Learn more: https://www.h2o.ai/
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