Fair Reinforcement Learning for Maternal Sepsis Treatment

Abstract

ABSTRACT Objectives Reinforcement Learning is a branch of artificial intelligence (AI) which has the potential to support significant improvement in patient care. There is concern that such approaches may reinforce existing biases within patient groups. Understanding discrimination in AI models is important for building trust and ensuring fair and safe use. We explore the fairness of a published reinforcement learning model, used to suggest drug dosages for sepsis treatment of patients in critical care, on whether it safe to use with maternal sepsis patients. Methods We evaluate the current model using by a) comparing the results for a group of patients with maternal sepsis against a matched control group and b) using random forests to explore feature importance in the model. Results Our results show that the original clinicians decisions and model suggestions were similar across cohorts. Our feature importance ranking shows high variance for many of the features. Discussion In medical settings, different subgroups may have specific clinical needs and require different treatment however, in the absence of a clinical consensus on the most appropriate treatment, AI algorithms that give consistent treatment to patients regardless of subgroup could be judged as the safest and fairest option. Conclusion Our experiments showed that the evaluated model gave the same treatment to maternal and non-maternal sepsis patients. The methods developed for evaluating fair reinforcement learning may be more generally applicable to understanding how clinical AI tools can be used for safely and fairly. Key Words: Machine learning, Maternal Sepsis Treatment, Patient Safety, Fairness

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was funded by UKRI

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Data Availability

All data produced in the present work are contained in the manuscript.

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