Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment (2407.19380v1)

Abstract: Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the lack of interpretability and interactivity for clinicians. To address these challenges, we propose the medical decision transformer (MeDT), a novel and versatile framework based on the goal-conditioned reinforcement learning paradigm for sepsis treatment recommendation. MeDT uses the decision transformer architecture to learn a policy for drug dosage recommendation. During offline training, MeDT utilizes collected treatment trajectories to predict administered treatments for each time step, incorporating known treatment outcomes, target acuity scores, past treatment decisions, and current and past medical states. This analysis enables MeDT to capture complex dependencies among a patient's medical history, treatment decisions, outcomes, and short-term effects on stability. Our proposed conditioning uses acuity scores to address sparse reward issues and to facilitate clinician-model interactions, enhancing decision-making. Following training, MeDT can generate tailored treatment recommendations by conditioning on the desired positive outcome (survival) and user-specified short-term stability improvements. We carry out rigorous experiments on data from the MIMIC-III dataset and use off-policy evaluation to demonstrate that MeDT recommends interventions that outperform or are competitive with existing offline reinforcement learning methods while enabling a more interpretable, personalized and clinician-directed approach.

• The paper presents a novel goal-conditioned framework that leverages offline reinforcement learning for personalized sepsis treatment recommendations.
• It integrates acuity scores for interactive decision support, enabling clinicians to tailor treatment plans based on specific target outcomes.
• A state prediction model validates treatment policies by modeling patient state transitions, demonstrating competitive performance on the MIMIC-III dataset.

Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment

The paper "Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment" introduces the Medical Decision Transformer (MeDT), an innovative framework designed to enhance clinical decision support in the treatment of sepsis. This work leverages goal-conditioned offline reinforcement learning (RL) to address challenges in sepsis management by offering interpretable and interactive recommendations for optimal drug dosage. The technical foundation of this paper is built upon the Decision Transformer (DT) architecture, exploiting its capabilities for temporal sequence modeling within the clinical setting.

Key Contributions

1. Goal-Conditioned Framework: MeDT is a novel implementation of the DT, specifically adapted for medical applications where decisions must be tailored to patient-specific outcomes. It conditions policy modeling on both historic and desired future states, enabling clinicians to input their expectations for short-term patient stability improvements.
2. Interactive Decision Support: The framework uses acuity scores to manage reward sparsity and facilitate clinician interaction during treatment planning. By incorporating features like target acuity scores, MeDT provides a structured approach for clinicians to specify treatment goals, resulting in interactive and personalized decision-making.
3. State Prediction Model: To evaluate its policy recommendations without direct patient interaction, MeDT incorporates a state predictor. This component models patient state transitions based on treatment actions, serving as a reliable proxy for direct evaluation of treatment policies.

Experimental Results

A comprehensive evaluation was conducted using the MIMIC-III dataset, involving rigorous off-policy evaluations through several methods such as FQE, WIS, and WDR. The empirical findings suggest that MeDT achieves competitive performance relative to established RL methods, such as BCQ and CQL, particularly excelling in patient test cases of low to moderate severity. Its conditioning on short-term goals translates into treatment trajectories with favorable clinical outcomes, outperforming baselines in key stability metrics.

Theoretical and Practical Implications

The fusion of transformer-based architectures with RL paradigms signifies a promising direction for developing interpretable AI systems in healthcare. MeDT exemplifies an approach where policy learning aligns closely with domain-specific human expertise, facilitating clinician trust and uptake. The framework's adaptability enables the application to other critical care scenarios, thereby extending its utility beyond sepsis treatment.

Future Directions

The paper opens avenues for further exploration into transformer-based RL applications in healthcare. Future work could focus on refining the interpretability mechanisms to offer more granular insights into model reasoning, potentially integrating causal inference to mitigate data limitation challenges inherent in healthcare datasets. Additionally, the development of strategies to improve sample efficiency in the presence of sparse data conditions could enhance model robustness across different patient demographics and treatment settings.

This research underscores the transformative potential of leveraging advanced sequence modeling techniques in clinical decision-making, paving paths towards more effective, personalized, and safer healthcare interventions.