Bayesian Networks for Health Economics and Public Policy Research
Recorded on November 15, 2018, at the NYU Kimmel Center in New York City.
In this seminar, we illustrate how Bayesian networks can serve as a powerful modeling and reasoning framework for health economics research and public policy development.
For five different case studies, we present a complete analysis workflow using the BayesiaLab 8 software platform:
- Diagnostic decision support: using a machine-learned Bayesian network for cost-effective evidence-seeking in diagnosing coronary heart disease. This example introduces information-theoretic measures, such as Entropy and Mutual Information.
- Quantifying the value of information in field triage for optimizing trauma activation thresholds with regard to hospital resource utilization.
- Developing universal health policies under extreme uncertainty, i.e., without any data: "test & treat" or presumptive malaria treatment in sub-Saharan Africa.
- Childhood Literacy Campaign: Simpson's paradox rears its ugly head and leads to misguided policies.
- Causal inference from observational healthcare data: using machine learning and the Disjunctive Cause Criterion to reduce—but not eliminate—the need for causal assumptions.
For each example, we present the motivation, proposed methodology, and practical implementation.
- Presentation Slides (PDF, 56.5 MB)
- Example 1: Diagnostic Decision Support
- Example 2: Trauma Activation Policy
- Example 3: Optimizing Health Policies Under Uncertainty
- BayesiaLab Network File (XBL, 2 KB)
- Example 5:
|At your convenience||On your desktop/laptop||Introductory Course (60-Day Self-Study Edition)|
|At your convenience||On your desktop/laptop||Advanced Course (60-Day Self-Study Edition)|
|March 24–26, 2020 — CANCELLED||Boston, MA, USA||3-Day Introductory Course|
|April 7–9, 2020 — CANCELLED||Paris, France||3-Day Introductory Course|
|May 6–8, 2020||Seattle, WA, USA||3-Day Introductory Course|
|May 11–13, 2020||Seattle, WA, USA||3-Day Advanced Course|
|June 15–17, 2020||Paris, France||3-Day Advanced Course|
|October 5–7, 2020||Toronto, ON, Canada||3-Day Introductory Course|
|October 13–15, 2020||Toronto, ON, Canada||3-Day Advanced Course|
Seminars, Webinars, and Conferences
|March 20, 2020||Free Webinar||Reasoning Under Uncertainty (Part 1): Differential Diagnosis of Diseases|
|March 26, 2020||Free Webinar||Reasoning Under Uncertainty (Part 2): Epidemic Modeling with Temporal Bayesian Networks|
|Please check out our archive of recordings of previous events in the BayesiaLab Community.|