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