Episode Sampling Module for Reinforcement Learning in Medical Treatment Pathways
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Solution Overview
Problem
Current medical AI technologies face challenges in applying reinforcement learning due to difficulties in collecting sufficient episodes for training, especially in medical environments where real patient data is costly and hard to obtain, leading to issues like reward over-estimation and inaccurate model simulations.
Innovation Solution
The proposed solution involves an apparatus and method for exploring optimized treatment pathways through model-based reinforcement learning using similar episode sampling, which includes an episode sampling module, state value evaluation module, treatment method learning module, and virtual episode generation module to predict and optimize treatment methods and timing by calculating similarities between virtual and real EMR episodes, thereby reducing errors and over-estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If model-based reinforcement learning is used to simulate real medical environments, then the cost of collecting episodes is reduced and virtual episodes can be generated, but the accuracy of state change prediction deteriorates due to model inaccuracy
Solution Approach 1:
The patent introduces a similarity calculation mechanism as an intermediary between the virtual model and real medical environments. By calculating similarity between virtual episodes and real episodes, the system selectively weights and combines predictions from both sources, allowing the inaccurate virtual model to be used cost-effectively while correcting its predictions using real data when available, thus resolving the contradiction between cost reduction and accuracy maintenance
Solution Approach 2:
The patent dynamically adjusts the weight parameters assigned to virtual episode predictions versus real episode predictions based on calculated similarity metrics. When virtual episodes show high similarity to real patterns, their predictions are weighted more heavily; when similarity is low, real data weights increase. This parameter adjustment strategy allows the system to leverage the cost advantages of virtual modeling while maintaining prediction accuracy through adaptive calibration
2Reliability
If episode-based reinforcement learning is used without model simulation, then there is no distortion from inaccurate models, but the direction of learning becomes unknown for situations except actually collected data
Solution Approach 1:
The patent creates virtual copies of real medical episodes through simulation, generating synthetic training data that mirrors real-world patterns. These copied virtual episodes are then used to augment the limited real data, allowing the reinforcement learning agent to learn from both authentic real episodes and realistic virtual replicas, thereby expanding learning coverage to situations not directly observed in real data while maintaining fidelity to actual medical scenarios
Solution Approach 2:
The patent performs preliminary generation of virtual episodes and pre-calculation of similarity metrics before the main reinforcement learning training process. By preparing these virtual training samples and similarity references in advance, the system establishes a foundation that guides subsequent learning, allowing the agent to have prior knowledge of potential situations and their expected outcomes, thus improving adaptability to uncollected scenarios without introducing model distortion during the actual learning phase
3Adaptability or versatility
If reinforcement learning is applied to medical environments, then optimized treatment pathways can be explored, but sufficient training episodes are difficult to collect due to high costs and rarity of identical patient states
Solution Approach 1:
The patent merges two previously separate approaches - model-based reinforcement learning and episode-based reinforcement learning - into a unified hybrid framework. By combining the virtual episode generation capabilities of model-based methods with the real data grounding of episode-based methods, the system achieves sufficient training episode quantity through the synergistic effect of virtual and real data, while maintaining the treatment pathway optimization capability that requires both model guidance and real-world validation
Data Source
AI summary
Disclosed is an apparatus for exploring an optimized treatment pathway of a target patient, which includes an episode sampling module that receives a virtual electronic medical record (EMR) episode, calculates a similarity between a first current state of the target patient, which corresponds to the received virtual EMR episode, and a second current state of a patient, which corresponds to each of a plurality of EMR episodes, extracts an EMR episode, and outputs a pair of the virtual EMR episode and the extracted EMR episode, a state value evaluation module that predicts an expected value of a reward, a treatment method learning module that predicts an optimized treatment method and optimized timing of treatment and provides an external prediction model with the current state of the target patient and the treatment method, and a virtual episode generation module that generates a new virtual EMR episode.