[Paper Review] Offline reinforcement learning with uncertainty for treatment strategies in sepsis

Ran Liu, Joseph L. Greenstein+4 more|arXiv (Cornell University)|Jul 9, 2021

Sepsis Diagnosis and Treatment44 references5 citations

TL;DR

This paper proposes an offline reinforcement learning framework with uncertainty estimation to derive personalized sepsis treatment strategies from retrospective electronic health record data. By mitigating bias toward under-treatment caused by confounding between mortality and treatment intensity, the method generates multiple high-confidence treatment options, improving accuracy and personalization in critical care decision-making.

ABSTRACT

Guideline-based treatment for sepsis and septic shock is difficult because sepsis is a disparate range of life-threatening organ dysfunctions whose pathophysiology is not fully understood. Early intervention in sepsis is crucial for patient outcome, yet those interventions have adverse effects and are frequently overadministered. Greater personalization is necessary, as no single action is suitable for all patients. We present a novel application of reinforcement learning in which we identify optimal recommendations for sepsis treatment from data, estimate their confidence level, and identify treatment options infrequently observed in training data. Rather than a single recommendation, our method can present several treatment options. We examine learned policies and discover that reinforcement learning is biased against aggressive intervention due to the confounding relationship between mortality and level of treatment received. We mitigate this bias using subspace learning, and develop methodology that can yield more accurate learning policies across healthcare applications.

Motivation & Objective

• Address the challenge of personalized sepsis treatment due to the heterogeneity and complexity of sepsis pathophysiology.
• Overcome the limitations of guideline-based protocols that often lead to overtreatment or undertreatment due to lack of individualization.
• Develop a reinforcement learning framework that learns from historical EHR data without online interaction, ensuring safety and scalability.
• Estimate uncertainty in treatment recommendations to identify rare or high-risk scenarios and improve clinical trust.
• Mitigate bias in learned policies that favor less aggressive treatment due to confounding between treatment intensity and mortality in observational data.

Proposed method

• Apply offline reinforcement learning to learn optimal treatment policies from retrospective EHR data of sepsis patients.
• Incorporate uncertainty estimation in Q-value predictions to identify low-confidence or rare treatment scenarios.
• Use subspace learning to decompose the state-action space and reduce bias from confounding factors such as mortality-treatment correlation.
• Train a deep Q-network with distributional output to model uncertainty in value predictions.
• Generate multiple treatment recommendations per patient instead of a single deterministic action, enabling clinical flexibility.
• Apply a confidence threshold to filter out low-reliability recommendations, improving safety and interpretability.

Experimental results

Research questions

• RQ1 How can offline reinforcement learning be adapted to learn reliable sepsis treatment policies from retrospective EHR data with minimal online interaction?
• RQ2 To what extent does confounding between treatment intensity and mortality bias reinforcement learning policies toward under-treatment?
• RQ3 Can uncertainty estimation improve the reliability and clinical interpretability of RL-based treatment recommendations in sepsis?
• RQ4 How effective is subspace learning in mitigating bias in offline RL when applied to complex, high-dimensional clinical data?
• RQ5 Can the framework generate multiple, high-confidence treatment options that support personalized decision-making in sepsis management?

Key findings

• The reinforcement learning policy learned from offline data exhibited a bias toward under-treatment due to the confounding relationship between high treatment intensity and increased mortality in the training data.
• Subspace learning significantly reduced this bias, resulting in more accurate and clinically plausible treatment policies.
• Uncertainty estimation successfully identified rare or high-risk treatment scenarios, enabling the system to flag low-confidence recommendations.
• The method generated multiple treatment options per patient, increasing clinical flexibility and personalization compared to single-action policies.
• The final policy demonstrated improved generalization across diverse patient subgroups, particularly in high-acuity sepsis cases.
• The framework achieved higher confidence in recommendations for patients with complex comorbidities, where standard guidelines often fail.