Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (1807.01473v2)

Abstract: Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e.g. maximizing evaluation signal which indicates cumulative reward from survival rates). However, none of these studies have considered to combine the benefits of supervised learning and reinforcement learning. In this paper, we propose Supervised Reinforcement Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a synergistic learning framework. Specifically, SRL-RNN applies an off-policy actor-critic framework to handle complex relations among multiple medications, diseases and individual characteristics. The "actor" in the framework is adjusted by both the indicator signal and evaluation signal to ensure effective prescription and low mortality. RNN is further utilized to solve the Partially-Observed Markov Decision Process (POMDP) problem due to the lack of fully observed states in real world applications. Experiments on the publicly real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the estimated mortality, while providing promising accuracy in matching doctors' prescriptions.

• The paper demonstrates that integrating supervised and reinforcement learning in SRL-RNN significantly reduces in-hospital mortality by 4.4% on the MIMIC-3 dataset.
• The model utilizes an LSTM to capture complete patient histories, effectively addressing incomplete state dynamics in clinical settings.
• SRL-RNN outperforms traditional methods such as D3Q, BL, and LEAP, providing more accurate and personalized treatment recommendations.

Essay on "Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation"

The paper "Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation" introduces a novel model aimed at optimizing the dynamic treatment recommendations in complex medical scenarios observed in Intensive Care Units (ICUs). The proposed model, SRL-RNN, seeks to leverage electronic health records (EHRs) to deliver personalized treatment plans by integrating supervised learning (SL) and reinforcement learning (RL) frameworks. This integration addresses the existing gap where traditional SL and RL approaches often lack the capability to exploit both indicator (prescriptions from doctors) and evaluation signals (clinical outcomes).

SRL-RNN Model Architecture

SRL-RNN's architecture applies an off-policy actor-critic framework augmented with Recurrent Neural Networks (RNNs) to manage dynamic treatment decision-making, particularly under the assumptions of a Partially-Observed Markov Decision Process (POMDP). The actor network, guided by dual signals (indicator and evaluation), recommends time-varying medication plans while considering complex relations among diseases, medications, and patient characteristics. Critically, SRL-RNN applies an RNN, specifically an LSTM, to encapsulate the entire historical observation of patient data, addressing the incomplete state dynamics typical in clinical settings.

Numerical Results and Claims

The application of SRL-RNN on the MIMIC-3 dataset revealed that incorporating both indicator and evaluation signals significantly enhances treatment recommendations. The model demonstrated a noteworthy 4.4% reduction in estimated in-hospital mortality rates compared to baseline models. Furthermore, classification accuracy, as measured by the Jaccard coefficient, also showed improvements, indicating a closer match to doctor-prescribed treatments than other models considered in the paper.

Comparative Analysis

Several existing methodologies including Dueling Double-Deep Q Learning (D3Q), Basic LSTM (BL), and LEAP were evaluated against SRL-RNN. The novel model consistently outperformed these methods, substantiating the claim that integrating SL and RL with historical data to solve POMDP yields superior dynamic treatment strategies.

Theoretical and Practical Implications

The SRL-RNN model raises notable implications for the evolution of AI in healthcare. Theoretically, it asserts the benefit of using a joint SL and RL approach to resolve complex sequential decision-making problems in partially observable environments. Practically, its deployment could significantly influence clinical settings by enhancing personalized patient care and reducing adverse outcomes through data-driven insights, shaping the model as an effective tool for AI-powered clinical decision support systems.

Speculative Future Directions

The research paves the way for further investigation into the convergence of machine learning paradigms for enhanced interpretability and robustness of predictive healthcare models. Future endeavors could explore the integration of additional data modalities, such as genomic data, to bolster predictive accuracy and explore human-centric AI systems that aim to replicate expert decision-making in more diverse, real-world medical scenarios.

The conclusions drawn in this paper suggest a promising direction for the adoption of AI in healthcare environments, as SRL-RNN offers a sophisticated framework for translating complex patient data into actionable insights for personalized treatment regimens.