Safety-driven design of machine learning for sepsis treatment

Machine learning (ML) has the potential to bring significant clinical benefits. However, there are patient safety challenges in introducing ML in complex healthcare settings and in assuring the technology to the satisfaction of the different regulators. The work presented in this paper tackles the urgent problem of proactively assuring ML in its clinical context as a step towards enabling the safe introduction of ML into clinical practice. In particular, the paper considers the use of deep Reinforcement Learning, a type of ML, for sepsis treatment. The methodology starts with the modelling of a clinical workflow that integrates the ML model for sepsis treatment recommendations. Then safety analysis is carried out based on the clinical workflow, identifying hazards and safety requirements for the ML model. In this paper the design of the ML model is enhanced to satisfy the safety requirements for mitigating a major clinical hazard: sudden change of vasopressor dose. A rigorous evaluation is conducted to show how these requirements are met. A safety case is presented, providing a basis for regulators to make a judgement on the acceptability of introducing the ML model into sepsis treatment in a healthcare setting. The overall argument is broad in considering the wider patient safety considerations, but the detailed rationale and supporting evidence presented relate to this specific hazard. Whilst there are no agreed regulatory approaches to introducing ML into healthcare, the work presented in this paper has shown a possible direction for overcoming this barrier and exploit the benefits of ML without compromising safety.

Automated summary

This paper discusses the importance of ensuring machine learning in clinical practice and uses deep Reinforcement Learning for sepsis treatment. It emphasizes the need for safety analysis and model design to meet clinical safety requirements, providing a basis for regulatory acceptance of ML technology.