Composite Loss Function for Clinical ML Model Training
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Solution Overview
Problem
Traditional machine learning models struggle to generate reliable and accurate recommendations in clinical scenarios, particularly for polychronic patients, as they often rely on outdated information and fail to balance between traditional policies and potential rewards, leading to either constrained or unconventional recommendations.
Innovation Solution
A composite loss function is introduced that balances expected outcome loss and imitation loss to train machine learning models, leveraging transformer architectures to capture long-range dependencies and synthesize disparate actions, ensuring recommendations are grounded in traditional policies while optimizing for potential rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning models are trained to maximize loss functions that do not account for traditional policies, then the model flexibility and potential rewards are improved, but the recommendations become unconventional and not grounded in traditional knowledge bases
Solution Approach 1:
The patent applies parameter changes by modifying the loss function parameters to include both expected outcome components and policy adherence components. The composite loss function L(θ) = L_expected_outcome(θ) + λL_policy(θ) changes the training parameters to balance reward optimization with policy grounding, resolving the contradiction between model flexibility and reliability in traditional policies.
Solution Approach 2:
The patent uses the composite materials principle by creating a composite loss function that combines two distinct loss components: expected outcome loss and policy adherence loss. This composite structure allows the model to simultaneously optimize for rewards while maintaining grounding in traditional policies, effectively resolving the contradiction between adaptability and reliability.
2Reliability
If machine learning models are trained to mirror traditional policies, then the recommendations are grounded in traditional knowledge bases, but the model becomes constrained to a portion of the prediction domain and cannot achieve optimal rewards
Solution Approach 1:
The patent applies dynamics by making the policy adherence constraint dynamic rather than static. Instead of rigidly mirroring traditional policies, the model dynamically balances policy adherence with reward optimization through the composite loss function. This allows the model to adapt to new situations while maintaining grounding in traditional policies, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The patent changes the training parameters by introducing a composite loss function that includes both policy adherence terms and expected outcome terms. This parameter modification allows the model to escape from being constrained to only mirror traditional policies, enabling it to achieve optimal rewards while maintaining sufficient grounding in traditional knowledge bases.
3Measurement precision
If traditional machine learning models rely on up-to-date, continuous, and accurate information, then the prediction accuracy is improved, but the models fail to handle irregularly observed data and large combinations of treatment actions
Solution Approach 1:
The patent applies universality by designing a transformer-based model that can handle multiple types of input data structures uniformly. The model processes both regularly spaced and irregularly observed clinical data through the same attention mechanism, enabling it to maintain prediction accuracy while adapting to irregular data patterns and large action spaces in clinical decision-making.
Data Source
AI summary
Various embodiments of the present disclosure provide machine learning training techniques for training a model to improve upon traditional prediction models for various prediction domains. The techniques may include receiving training tuples for a training entity. A machine learning model may be used to generate a prediction output for the training entity based on the training tuples. A composite loss function may be used to generate a composite loss metric for the machine learning model that is based on (i) a first loss metric based on a comparison between the prediction output and a plurality of historical reward measures and (ii) a second loss metric based on a comparison between the prediction output and an imitation output corresponding to the prediction output. One or more model parameters of the first machine earning model may be modified based on the composite loss metric.