Reinforcement Learning Model for Medical Data
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Solution Overview
Problem
In the medical field, determining appropriate medical actions for a patient's current state is challenging due to differences in professional experience and knowledge, and is further complicated by limited medical data, requiring significant time and effort.
Innovation Solution
A method involving the generation of virtual events based on actual medical data events, determining the probability of these virtual events, and training a model using reinforcement learning to determine state values for rewards of actions performed in a patient's current state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is applied to determine medical actions, then the objectivity and consistency of medical decision-making are improved, but the accuracy of action determination deteriorates due to limited medical data
Solution Approach 1:
The patent creates virtual copies of actual medical events by generating virtual events from real medical data. These virtual events replicate the structure and characteristics of actual events while providing additional training data. The copying process involves extracting features from real events and generating synthetic variations that maintain medical plausibility, thereby expanding the limited dataset without requiring additional real patient data.
Solution Approach 2:
The patent performs preliminary data preparation by generating virtual events before the actual reinforcement learning training. This preliminary action involves creating an expanded dataset that combines real and synthetic events, which is then used to pre-train the model. This preliminary training with augmented data prepares the model to handle the limited real data more effectively, improving subsequent learning accuracy.
2Measurement precision
If virtual events are generated to expand training data, then the accuracy of model training is improved, but reward overestimation occurs due to unrealistic virtual scenarios
Solution Approach 1:
The patent applies parameter changes by adjusting the probability weights of different event types during training. Virtual events are assigned different probability parameters compared to real events, allowing the model to learn from synthetic data while accounting for its artificial nature. This parameter adjustment prevents the model from overvaluing rewards from unrealistic scenarios while still benefiting from the additional training data.
Solution Approach 2:
The patent implements feedback mechanisms that monitor and adjust reward estimates based on the source of events. The system provides feedback signals that distinguish between real and virtual event outcomes, allowing the model to learn appropriate reward valuation. This feedback loop prevents reward overestimation by continuously adjusting the model's understanding of what constitutes a meaningful reward based on event authenticity.
3Reliability
If only actual medical data is used for training, then the reliability of training data is improved, but the generalization capability deteriorates due to data scarcity
Solution Approach 1:
The patent merges real medical events with generated virtual events into a unified training dataset. This combination strategy integrates the reliability of actual clinical data with the diversity and quantity of synthetic data. The merging process maintains proper weighting and attribution between real and virtual events, allowing the model to learn from both authentic medical scenarios and expanded variations that improve generalization to unseen cases.
Data Source
AI summary
An electronic device for reinforcement learning related to medical data and a method of operating the same are provided. The method includes obtaining an actual event related to medical data of a patient and a virtual event generated based on the actual event, determining a probability of the virtual event occurring, based on the medical data, and based on the actual event, the virtual event, and the probability of the virtual event, training a model to determine a state value for a reward of an action that is performed in a current state of the patient.