# A hybrid Bayesian network for medical device risk assessment and management

arXiv: https://arxiv.org/abs/2209.03352 · HTML: https://mlsanity.com/paper/2209.03352/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 74 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities](https://mlsanity.com/paper/2008.08221/) (2020)
- [A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future](https://mlsanity.com/paper/2002.08627/) (2020)
- [Review of The Book of Why: The New Science of Cause and Effect](https://mlsanity.com/paper/2003.11635/) (2020)
- [Product safety idioms: a method for building causal Bayesian networks for product safety and risk assessment](https://mlsanity.com/paper/2206.02144/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Bayesian Network Approach for Enhancing Security-Focused Decision Support Systems](https://mlsanity.com/paper/2606.10782/) (2026)
- [Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network Models](https://mlsanity.com/paper/2402.15075/) (2024)

## Abstract (arXiv)

ISO 14971 is the primary standard used for medical device risk management. While it specifies the requirements for medical device risk management, it does not specify a particular method for performing risk management. Hence, medical device manufacturers are free to develop or use any appropriate methods for managing the risk of medical devices. The most commonly used methods, such as Fault Tree Analysis (FTA), are unable to provide a reasonable basis for computing risk estimates when there are limited or no historical data available or where there is second-order uncertainty about the data. In this paper, we present a novel method for medical device risk management using hybrid Bayesian networks (BNs) that resolves the limitations of classical methods such as FTA and incorporates relevant factors affecting the risk of medical devices. The proposed BN method is generic but can be instantiated on a system-by-system basis, and we apply it to a Defibrillator device to demonstrate the process involved for medical device risk management during production and post-production. The example is validated against real-world data.