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.
Who should attend?
Biostatisticians, clinical scientists, data scientists, decision scientists, demographers, ecologists, econometricians, economists, epidemiologists, knowledge managers, management scientists, market researchers, marketing scientists, operations research analysts, policy analysts, predictive modelers, research investigators, risk managers, social scientists, statisticians, plus students and teachers of related fields.
Registration required. Please disregard the closed webform below and instead RSVP here.