Beyond the AI Buzz: Pragmatic Applications of Big Data and AI in Population Health - Grand Rounds 3/9/23

Beyond the AI Buzz: Pragmatic Applications of Big Data and AI in Population Health - Grand Rounds 3/9/23

Johns Hopkins Medicine via YouTube Direct link

TYPES OF ARTIFICIAL INTELLIGENCE

15 of 22

15 of 22

TYPES OF ARTIFICIAL INTELLIGENCE

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Beyond the AI Buzz: Pragmatic Applications of Big Data and AI in Population Health - Grand Rounds 3/9/23

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  1. 1 Intro
  2. 2 Population Health Informatics vs. Public Health Informa and Clinical Informatics
  3. 3 Health Care and Population Health Is Surrounded by an "e-Health" Digital Ecosystem
  4. 4 Social Determinants of Health (SDOH) are more important than medical care when it comes to human wellbeing
  5. 5 Social Determinants of Health and Population Health Analytics: Dat Sources, Applications and Context
  6. 6 Working Definitions
  7. 7 The Overlapping Data Science Fields: Ai, ML, Big Data, Predictive Analytics and More there is not always consensus regarding terms
  8. 8 The Pillars, Processes and Target Outcomes of a Value-Based Learning Health System Analytics and Big Data are Key
  9. 9 The Big Data Phases Applying Predictive Modeling: From Raw Data to Knowledge
  10. 10 Big Data "Volume" Challenges in Population Health
  11. 11 Big Data "Variety" Challenges in Population Health
  12. 12 Big Data "Velocity" Challenges in Population Health
  13. 13 Big Data "Veracity" Challenges in Population Hea
  14. 14 Working Definition of Artificial Intelligence and Machine Learning
  15. 15 TYPES OF ARTIFICIAL INTELLIGENCE
  16. 16 "Deep Learning" (aka neural nets) uses brute force to look for layer of "hidden" relationships between inputs (aka features) and outputs
  17. 17 In addition to predictions, ML can be applied to other types of pop heal analytics: "feature" (ind. variable) discovery, classification and selection are key
  18. 18 Comparison of AUC of ML and standard regression: One stud predicting chronic care outcomes - Model Accuracy is similar
  19. 19 CPHIT Study comparing standard regression to ML technique for predicting costs
  20. 20 At CPHIT we linked a range of medical, public health and hur service databases in Maryland to better predict opioid overdoses
  21. 21 Let's not forget that Ai and other types of analytics can be a source of bias or harm, leading to a type of e-iatrogenesis
  22. 22 Some key data science related challenges and opportunities in

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