Physiological Indicator Prediction Using Kernel Density Weighting
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Solution Overview
Problem
Medical providers face challenges in identifying rare conditions due to small sample sizes, which are difficult for processors to analyze effectively, especially when dealing with imbalanced medical time series data.
Innovation Solution
A system and method for predicting physiological indicators using a processor that receives time series inputs, fits prediction training data with a kernel density estimate, and trains a prediction machine learning model to weight contributions based on the abundance of continuous value labels, incorporating uncertainty metrics to improve accuracy in imbalanced datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional diagnostic techniques are used on small sample sizes, then processing time is reduced, but measurement precision and reliability of predictions deteriorate
Solution Approach 1:
The patent transforms the training approach by changing parameters of the machine learning model, specifically using kernel density estimates to weight training examples according to the abundance of continuous value labels. This parameter transformation allows the model to learn more effectively from imbalanced small sample sizes, improving prediction accuracy without requiring additional processing time
Solution Approach 2:
The patent replaces traditional statistical analysis methods with a machine learning-based prediction system. By substituting conventional diagnostic processing with a trained prediction model that incorporates uncertainty metrics and kernel density weighting, the system achieves higher measurement precision while maintaining efficient processing speeds
2Device complexity
If imbalanced medical time series data is analyzed using conventional methods, then device complexity is minimized, but reliability of identifying rare conditions deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning model with kernel density estimates that account for the imbalanced distribution of continuous value labels. This preliminary weighting of training data ensures that rare conditions are adequately represented in the model's learning process, improving diagnostic reliability before actual deployment without adding complexity to the operational system
Solution Approach 2:
The patent introduces kernel density estimates as an intermediary mechanism between the imbalanced training data and the prediction model. This intermediary weighting system mediates the influence of rare versus common conditions, allowing the model to learn appropriate patterns from imbalanced data while maintaining system architecture simplicity
3Measurement precision
If kernel density estimate weighting is applied to training data, then measurement precision of predictions improves, but device complexity increases
Solution Approach 1:
The patent implements parameter changes by modifying the training data weights through kernel density estimates based on the abundance of continuous value labels. This parameter transformation is integrated into the standard machine learning training pipeline, improving prediction accuracy while maintaining relatively simple model architecture and training procedures
Data Source
AI summary
An apparatus for predicting a physiological indicator is disclosed. The apparatus comprises a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a time series input from a user. The memory instructs the processor to predict a physiological indicator as a function of the time series input using a prediction machine learning model. The memory instructs the processor to generate an uncertainty metric as a function of the physiological indicator. Wherein determining the physiological indicator includes receiving a plurality of prediction training data. Determining the physiological indicator includes fitting the plurality of prediction training data with a kernel density estimate. Determining the physiological indicator additionally includes training the prediction machine learning model using a plurality of prediction training data. Training the prediction machine learning model includes, using the kernel density estimate, assigning a weight to each time series input.