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Risk management for nuclear medical department using reinforcement learning algorithms

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Abstract

Modern medical software systems are often classified as medical devices and governed by regulations which require stringent risk safety activities to be implemented to minimize the occurrence of risky events. This paper proposes a reinforcement learning (RL) based approaches for training a software agent for risk management of medical software systems. The goal of RL agent is to avoid that a patient enters in dangerous and undesirable states. At the same time agent must be able to reach on a safe state or an exit in a minimum interval of time. RL based system is also able to guide a patient to a safe path if he/she mistakenly enter into risk or undesirable states.

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Authors and Affiliations

  1. ICAR-CNR, Naples, Italy

    Giovanni Paragliola & Muddasar Naeem

  2. Department of Engineering, University of Parthenope, ICAR-CNR, Naples, Italy

    Muddasar Naeem

Authors
  1. Giovanni Paragliola
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  2. Muddasar Naeem
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Correspondence to Giovanni Paragliola.

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Paragliola, G., Naeem, M. Risk management for nuclear medical department using reinforcement learning algorithms. J Reliable Intell Environ 5, 105–113 (2019). https://doi.org/10.1007/s40860-019-00084-z

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  • DOI: https://doi.org/10.1007/s40860-019-00084-z

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