Preference-Sensitive Management of Post-Mammography Decisions in Breast Cancer Diagnosis.

ABSTRACT: Decision models representing the clinical situations where treatment options entail a significant risk of morbidity or mortality should consider the variations in risk preferences of individuals. In this study, we develop a stochastic modeling framework that optimizes risk-sensitive diagnostic decisions after a mammography exam. For a given patient, our objective is to find the utility maximizing diagnostic decisions where we define the utility over quality-adjusted survival duration. We use real data from a private mammography database to numerically solve our model for various utility functions. Our choice of utility functions for the numerical analysis is driven by actual patient behavior encountered in clinical practice. We find that invasive diagnostic procedures such as biopsi

SUBMITTER: Ayvaci MUS 

PROVIDER: S-EPMC6481963 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature