An Explainable Deep Reinforcement Learning Model for Optimizing Warfarin Maintenance Dosing Using Policy Distillation and Action Forging

Основні поняття

An explainable deep reinforcement learning model is proposed to optimize warfarin maintenance dosing by combining Proximal Policy Optimization, Policy Distillation, and novel "Action Forging" techniques to achieve a dosing protocol that is easy to understand and deploy while outperforming baseline dosing algorithms.

Анотація

The paper presents an explainable deep reinforcement learning (DRL) model for optimizing warfarin maintenance dosing. The problem is formulated as a Markov Decision Process, where the goal is to find the optimal sequence of dose adjustments to keep the patient's International Normalized Ratio (INR) within the therapeutic range.
The key aspects of the proposed approach are:

Proximal Policy Optimization (PPO) is used to train a DRL model that learns the optimal maintenance dosing policy.

"Action Forging" techniques are introduced to modify the action space and shape the action distribution to achieve better explainability. These include:

Action regularizer: Discretizes the action space (percent dose change) to a smaller set of pre-determined values.
Action focus: Increases the probability of the "no dose change" action compared to small dose changes using a wavelet function.

Policy Distillation is then used to extract an explainable dosing protocol from the trained DRL model in the form of a simple dosing table with INR ranges and corresponding percent dose changes.

The proposed explainable DRL model is evaluated against baseline dosing protocols and shown to outperform them in terms of Percent Time in Therapeutic Range (PTTR), a key performance metric for warfarin dosing. The final explainable dosing protocol has only 3 possible actions, making it easy to understand and deploy in practice.

Статистика

The average PTTR of the proposed explainable DRL model is 78.0% across all sensitivity levels, outperforming the baseline Aurora (68.8%) and Intermountain (56.6%) protocols.

Цитати

"The promising dosing protocol that we proposed here should be considered as an example of how machine learning models can be transformed to satisfy the need for interpretable, easy to understand, and easy to use medical solutions."
"Action Forging is a useful technique that can take on many forms. We only presented two techniques, action regularizer and action focus, that were necessary in making the final model more explainable. Depending on the use case, novel forging techniques can be developed."

Ключові висновки, отримані з

An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging

by Sadjad Anzab... о arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17187.pdf

Глибші Запити

How can the proposed "Action Forging" techniques be extended to handle the decision of dose duration in addition to dose change

To extend the "Action Forging" techniques to handle the decision of dose duration in addition to dose change, we can introduce a similar approach focusing on the duration aspect. Just like we emphasized the 0% dose change action, we can prioritize certain durations over others. This can be achieved by modifying the action probabilities related to duration choices based on the model's training progress. By applying a similar wavelet function to the duration probabilities, we can guide the model to prefer certain durations, such as longer intervals between dose adjustments, when appropriate. This would involve adjusting the action space to include specific duration options and influencing the model to select those durations more frequently during training.

What alternative reward functions could be explored to influence the model's selection of decision boundaries within the therapeutic range

Exploring alternative reward functions to influence the model's selection of decision boundaries within the therapeutic range can provide insights into different aspects of patient care. One alternative reward function could focus on the rate of change in INR values rather than the absolute distance from the target range. By penalizing rapid changes in INR values, the model would be encouraged to make more gradual adjustments to the dose, promoting stability in the patient's response to warfarin. Additionally, a reward function that considers the individual patient's tolerance to fluctuations in INR levels could be beneficial. This personalized approach could help the model tailor dosing decisions based on each patient's specific characteristics and response patterns.

How would the explainable dosing protocol perform on a more diverse patient population beyond the simulated cohort used in this study

The performance of the explainable dosing protocol on a more diverse patient population beyond the simulated cohort used in this study would depend on the generalizability of the model. If the model has been trained on a sufficiently diverse dataset that captures a wide range of patient characteristics, genetic factors, and responses to warfarin, it is likely to perform well on a more diverse population. However, challenges may arise if the model has not been exposed to a broad enough range of patient profiles during training. In such cases, the explainable dosing protocol may need to be further validated and fine-tuned using real-world data from diverse patient populations to ensure its effectiveness and reliability across different demographic groups.