This course gives beginners an introduction to modern tools for diagnosing disease with patient data. The course covers how Big Data is impacting healthcare, statistical tools for modeling patient data, some applications of these tools, and the future of healthcare (and philosophical implications) regarding AI. https://www.experfy.com/training/courses/an-introduction-to-diagnosing-diseases-with-patient-data

2. An Introduction to Diagnosing
Diseases with Patient Data
John Sukup
Principal Consultant, Expected X

3. John Sukup
Principal Consultant, Expected X
●11 Years in research and data
analytics with The Nielsen Company,
BlueCross BlueShield, and others
●Model development for Department
of Energy’s SPR and Telemedicine
Impact Score projects
●Business experience across several
industries including healthcare, CPG,
energy, financial services, insurance,
consumer tech, and manufacturing
●Expertise in data modeling, Big Data
solutions consulting, Python, R,
Microsoft Cognitive Services, etc.
●MS/BS Purdue University and
several professional certifications

4. MODULE 1
Introduction to Course

6. •How data is shaping the modern world
•The relationship between data and
technology
•What role data plays in healthcare and
disease diagnosis
•Intended audience
What is this course all about?

7. What are some of the topics I
should be familiar with?
•Basic statistics and probability
•Basic data analytics practices
•Programming and its role in data analytics

8. •Analytical techniques and their applications
in disease diagnosis
•Foundational concepts of Big Data
•Big Data solutions in modern healthcare
What should I have learned by
the end?

9. How is the course structured?
•Outline of course sections
•Using the course discussion board

10. MODULE 2
Making Sense of (Big) Data

11. SECTION 1
Data in the 21st century: Where is it all coming
from?

12. Brief history of data
Data in the 21st century: Where is it all coming from? 12

13. Section Name here 13

14. Brief history of data
14Data in the 21st century: Where is it all coming from?

15. Data’s meteoric increase in the 21st century
15Data in the 21st century: Where is it all coming from?
A 2003 study by UC Berkeley,
researchers found…
● ...5 exabytes of new information
produced every year
● ...new, stored information grew
30% between 1999 - 2002
● ...18 exabytes of data produced but
not stored

16. Data’s meteoric increase in the 21st
century
16Data in the 21st century: Where is it all coming from?

17. Sources of data
17Data in the 21st century: Where is it all coming from?

18. Sources of data
18Data in the 21st century: Where is it all coming from?

19. SECTION 2
What makes Big Data “big?”

20. The “V’s” of Big Data
What makes Big Data “big?” 20
VOLUME VELOCITY VARIETY

21. The “V’s” of Big Data
What makes Big Data “big?” 21
VERACITY VALUE VOLATILITY

22. The “V’s” of Big Data
What makes Big Data “big?” 22

23. • Big Data is inherently unmanageable on localized systems and
traditional relational databases
o Storage, compute, distribution
• Scalability and cloud platforms
o Static vs. dynamic, fault tolerance, plug-and-play XaaS
• Amazon Web Services, Google Cloud Platform, Microsoft
Azure
Information technology systems and Big
Data
What makes Big Data “big?” 23

24. Information technology systems and Big
Data
What makes Big Data “big?” 24

25. • Competitive advantage
o Customer understanding and
targeted, personalized delivery
o Real-time decision making
o Process automations
o New revenue stream generation
o New product/service
development
o Long/short-term cost savings
What Big Data is and isn’t
What makes Big Data “big?” 25

26. What Big Data is and isn’t
What makes Big Data “big?” 26

27. • “No Free Lunch!”
• A tool, not a panacea
• Parlor tricks
• Limited to big businesses
• Programming languages
• For Ph.Ds only
What Big Data is and isn’t
What makes Big Data “big?” 27

28. What Big Data is and isn’t
What makes Big Data “big?” 28
• Artificial
intelligence/Machine
learning
• Easy to “dive right into”
• Expensive (necessarily)
• Set it and forget it
• Dashboards and
visualizations

29. • Data security
• Data provenance
• Data representation
• Data privacy
• Model bias
• Model transparency
Data governance
What makes Big Data “big?” 29

30. • Affordable Care Act (ACA)
• Health Information Technology for Economic and Clinical
Health (HITECH)
• Health Insurance Portability and Accountability Act (HIPAA)
Privacy and Security Rules
• State laws
HIPAA and other legal considerations
The importance of data in disease diagnosis: Can an algorithm beat a doctor? 30

31. SECTION 3
The importance of data in disease diagnosis: Can an algorithm
beat a doctor?

32. Data’s traditional place in healthcare
The importance of data in disease diagnosis: Can an algorithm beat a doctor? 32

33. Data’s traditional place in healthcare
The importance of data in disease diagnosis: Can an algorithm beat a doctor? 33

34. How big data is changing healthcare
The importance of data in disease diagnosis: Can an algorithm beat a doctor? 34

35. Diagnosing disease: Man vs. machine
The importance of data in disease diagnosis: Can an algorithm beat a doctor? 35

36. Diagnosing disease: Man vs. machine
The importance of data in disease diagnosis: Can an algorithm beat a doctor? 36
• Fatigable
• Singular consciousness
• Lengthy training
• High cost, low scale
• Lost knowledge on
retirement
• Biases
• Human
• Indefatigable
• Collective “consciousness”
• Continuous training
• Low(er) cost, high scale
• Persistent knowledge
• Biases
• Robot

37. Unit Quiz