D. Corrales Alonso, D. Ríos Insua
Developing cancer risk models enables the integration of predictions into decision support tools for personalized screening and treatment recommendations. By
modelling the impact of key medical variables on cancer risk, these tools can
identify risk groups which can guide the design of screening and treatment
programs. In this work, we present a decision analysis-based framework for personalized cancer screening, supporting decisions concerning whether and which
screening method to consider. The approach leverages an influence diagram
to model cancer risk and considers comfort, costs, complications and information as decision criteria, which are then integrated through a multi-attribute utility model. The resulting model supports the assessment of current screening programs, the design of novel strategies and the benchmarking of emerging screening methods.
Keywords: Decision analysis, influence diagram, cancer screening
Scheduled
Interdisciplinary applications of Bayesian methods
June 10, 2025 3:30 PM
Sala de prensa (MR 13)