Clinical ML Output Adaptation for Cross-Population Accuracy
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Solution Overview
Problem
Machine learning models trained on specific populations may not perform well when applied to different populations, requiring resource-intensive re-training or output combinations that lack transparency and regulatory challenges.
Innovation Solution
Integrate an adaptation module comprising a second machine learning model to adjust the output of a first model based on new training data, preserving the original model's topology while incorporating additional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained on a specific patient population, then it produces accurate predictions for that population, but it fails to perform accurately on different patient populations
Solution Approach 1:
The patent introduces an adaption module as an intermediary component between the original machine learning model and the new patient population data. This adaption module is trained on the second patient population data and adjusts the model's outputs specifically for that population, allowing the original model to maintain its accuracy for its training population while the adaption module handles the population-specific adjustments.
Solution Approach 2:
The patent segments the machine learning system into two distinct components: the original model trained on the first patient population and a separate adaption module trained on the second patient population. This segmentation allows each component to specialize in its respective population while working together to provide accurate predictions across multiple populations.
2Measurement precision
If a new machine learning model is trained for each patient population, then accurate predictions are achieved for each population, but resource consumption and model complexity increase significantly
Solution Approach 1:
The patent combines multiple population-specific functionalities into a single integrated system. Instead of maintaining separate models for each population, the original model and adaption module work together as one unified system that can handle multiple populations, reducing the number of separate models that need to be developed, validated, and deployed.
Solution Approach 2:
The adaption module serves as a universal component that can be applied to adjust the original model for different patient populations. This multi-functional approach allows a single adaption module structure to handle various population adjustments, reducing the need to create entirely new models for each population.
3Measurement precision
If a new machine learning model is trained for each patient population, then population-specific accuracy is improved, but regulatory approval and integration challenges increase
Solution Approach 1:
The patent performs preliminary adaptation by training the adaption module on the second patient population data before deployment. This preliminary action ensures that the model is already adjusted for the specific population characteristics, reducing the need for post-deployment adjustments and simplifying regulatory validation since the adaptation process is predetermined and documented.
4Adaptability or versatility
If stacking is used to combine outputs of multiple models, then new conditions and features are incorporated, but transparency and trust in outputs decrease
Solution Approach 1:
The adaption module acts as a transparent intermediary that clearly adjusts the original model's outputs based on the second patient population data. Unlike stacking which combines multiple opaque model outputs, the adaption module provides a single, interpretable adjustment layer that maintains the connection between the original model's predictions and the final adjusted predictions, preserving transparency and trust.
Data Source
AI summary
Systems and methods for adapting a first machine learning model that takes clinical data as input, based on a second set of training data. The first machine learning model having been trained on a first set of training data. The method comprises adding an adaption module to the first machine learning model, the adaption module comprising a second machine learning model, and training the second machine learning model using a second set of training data to take an output of the first machine learning model as input and provide an adjusted output.