Population Gaussian Process Clinical Forecasting
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Solution Overview
Problem
Conventional mechanisms for predicting patient clinical variables lack accuracy due to failure in extracting patterns from vast data sets and reliance on parametric assumptions or post-processing heuristics, leading to suboptimal prediction models.
Innovation Solution
A method involving the collection of multi-dimensional clinical time series from a training population to train a machine learning algorithm, which generates a prediction model for a test patient by using patterns learned from the training population, incorporating sparse linear combinations of latent Gaussian processes to forecast future clinical variable values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patient-specific models are used for prediction, then the model can be trained on individual patient data, but the prediction accuracy deteriorates because patterns from training population data are not extracted
Solution Approach 1:
The patent combines patient-specific data with population-level training data into a unified prediction framework. The system merges individual patient time series with aggregated patterns from multiple patients, allowing the model to benefit from both personalized observations and population-wide statistical regularities, thereby resolving the contradiction between adaptability and accuracy
Solution Approach 2:
The prediction system is designed to serve multiple functions: it can operate with patient-specific data alone, with population data alone, or in combination. This multi-functional approach allows the same system to adapt to different data availability scenarios while maintaining high prediction accuracy through the optional integration of population-level patterns
2Quantity of substance
If conventional mechanisms use training population to generate prediction models, then more data is available for training, but the prediction accuracy deteriorates due to reliance on parametric assumptions and post-processing heuristics
Solution Approach 1:
The patent replaces conventional statistical methods (parametric assumptions and heuristic post-processing) with a neural network-based machine learning system. This substitution allows the model to automatically learn complex patterns from large-scale training data without relying on restrictive parametric assumptions, thereby utilizing the full potential of population-level data while maintaining high prediction accuracy
Solution Approach 2:
The system transforms the approach to handling training data by changing from fixed parametric models to adaptive neural network parameters. The neural network learns optimal parameters directly from the data distribution, allowing the model to capture complex temporal patterns and relationships that traditional parametric methods cannot, thus improving accuracy while utilizing large training datasets
3Ease of operation
If patient-specific models are used, then individual patient data is utilized, but the model fails to extract patterns from immense available data from other patients
Solution Approach 1:
The system introduces an intermediary component that bridges patient-specific data and population-level patterns. This intermediary layer processes and integrates information from both sources, allowing the model to retain the simplicity of individual patient modeling while simultaneously capturing rich patterns from the training population, thus preventing information loss without complicating the operational simplicity
Data Source
AI summary
A device, system and method for generating a prediction model for a test patient. To generate the prediction model, a multi-dimensional clinical time series for each of a plurality of training patients is collected to generate a training population. A machine learning algorithm is then trained using the training population. Measurement data corresponding to the test patient is also received, the measurement data includes a multi-dimensional clinical time series for the test patient. The test patient is not included in the plurality of training patients. The prediction model is generated for the test patient based on i) the measurement data corresponding to the test patient and ii) training the machine learning algorithm using the training population.