Mortality Prediction via Probability Density Ratio Estimation
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Solution Overview
Problem
Existing patient record management systems face challenges in accurately predicting mortality due to imbalanced datasets, where a small number of records in one category (mortality) compared to another (survival) lead to classification errors.
Innovation Solution
A method and system that extract historical data from patients, categorize it based on mortality status, determine test parameters, and calculate a probability score to predict mortality by comparing the score to a threshold, effectively handling imbalanced datasets by transforming Gaussian random variables into Chi-squared random variables for improved separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification models are used on imbalanced patient data, then the system is simple to implement, but the classification accuracy deteriorates due to minority class errors
Solution Approach 1:
The patent transforms the classification problem by changing the parameter space from direct class prediction to probability density ratio estimation. By computing the ratio of probability densities between majority and minority classes, the system handles imbalanced data without requiring complex resampling or cost-sensitive learning approaches, thus improving accuracy while maintaining relative simplicity
Solution Approach 2:
The patent introduces an intermediary approach by using probability density estimation as a mediator between the imbalanced data and the final classification decision. Instead of directly classifying imbalanced data, the system estimates probability densities and computes their ratios, which serves as an intermediate step that resolves the imbalance issue before making the final prediction
2Measurement precision
If the model focuses on the minority mortality category to improve detection, then the mortality prediction accuracy improves, but the overall classification performance deteriorates due to lack of majority class representation
Solution Approach 1:
The patent changes the parameter from direct class labels to probability density values. By estimating the probability density for each class and computing their ratio, the system naturally balances the contribution of both majority and minority classes, improving mortality prediction while maintaining overall classification reliability through the probabilistic framework
Solution Approach 2:
The patent moves the classification problem from the original class label dimension to a probability density dimension. By working in this transformed space where density ratios are computed, the system can simultaneously consider both majority and minority class information, resolving the trade-off between detecting rare events and maintaining overall performance
Data Source
AI summary
Disclosed are embodiments of methods and systems for predicting mortality of a first patient. The method comprises categorizing a historical data into a first category and a second category. The method further comprises determining a first test parameter and a second test parameter based on at least one of a sample data of a first patient and the historical data corresponding to at least one of the first category and the second category. The method further comprises determining a probability score based on a cumulative distribution of at least one of the first test parameter and the second test parameter. The method further comprises categorizing the sample data in one of the first category and the second category based on the probability score. Further, the method comprises predicting the mortality of the first patient based on at least the categorization of the sample data of the first patient.