Bayesian-Based Parameter Estimation to Quantify Trust in Medical Devices

In this paper, we propose a data-driven approach to estimate Bayesian parameters when trust needs to be quantified in the domain of wearable medical devices (WMD). Our approach extracts the probability of a trust determinant (e.g., reliability or robustness) being in a specific state from the data. Then, we use the Bayesian approach to estimate the parameters for the intermediate nodes in the network and ultimately compute the trust score. The trust score we compute is used as a relative measure of trustworthiness between different WMDs evaluated in the same test conditions and with the same Bayesian network (BN). To evaluate our approach, we develop a BN for the trust quantification of similar wearable medical devices from two manufacturers under identical test conditions. The results demonstrate the learnability and generalizability of our data-driven parameter estimation approach.