Decision Support Algorithm at the Life Cycle Stages of Medical Devices Based on the Application of Markov Process

With the development of information technology, large volumes of data are generated at all stages of the product life cycle, which need to be managed - collected, transmitted, and processed - to support decision-making. Consideration of the uncertainties associated with relevant information is important in risk analysis and management. This is especially true in decision-making due to the diversity of stakeholders at life cycle stages and the resulting different attitudes towards expected benefits. The decision to use a medical device in the context of a specific clinical procedure requires that residual risks be balanced against the expected benefits of the procedure. In this paper, the uncertainties that arise are modeled based on the Markov Chain theory. The maximum likelihood method is used. The problem of refining estimates of the transition probabilities of a Markov chain is formulated as a quadratic programming problem. The problem of constructing an algorithm is being solved, which allows using all the information to estimate the values of transition probabilities and has an iterative structure of the calculation process. Research results can be used to manage the risks associated with the use of medical devices at life cycle stages.