Data-driven approach to quantify trust in medical devices using Bayesian networks.
Exp Biol Med (Maywood)
; 248(24): 2578-2592, 2023 Dec.
Article
en En
| MEDLINE
| ID: mdl-38281083
ABSTRACT
Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our approach extracts the probability of a trust factor being in a specific state directly from the devices (e.g. sensor quality). The strength of the relationship between related factors is defined by expert knowledge and incorporated into the model. We use propagation rules from requirements engineering to estimate how much each trust factor contributes to the related intermediate nodes in the network and ultimately compute the trust score. The trust score is a relative measure of trustworthiness when different devices are evaluated in the same test conditions and using the same Bayesian structure. To evaluate our approach, we developed Bayesian networks for the trust quantification of similar wearable devices from two manufacturers under identical test conditions and noise levels. The results demonstrated the learnability and generalizability of our approach.
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Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Confianza
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Exp Biol Med (Maywood)
Asunto de la revista:
BIOLOGIA
/
FISIOLOGIA
/
MEDICINA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Canadá