Dynamic Weighting for Patient Outcome Prediction
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Solution Overview
Problem
Current patient recovery/mortality prediction systems, such as the SAPS system, are less accurate when run after the first day of admission, as they fail to account for the changing significance of clinical criteria over time, leading to inadequate weighting of vital signs and chronic diseases.
Innovation Solution
A system that updates predictions in real-time by using a patient information server to collect and process physiological, laboratory, and patient data, applying a time-dependent outcome variable estimation algorithm to adjust weighting factors based on the patient's duration of stay, incorporating data from past cases to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the same prediction routine and weighting of criteria are used for all days of admission, then the system is simple to operate, but the prediction accuracy deteriorates after the first day
Solution Approach 1:
The patent applies dynamics by making the weighting factors time-dependent. Instead of using static weights for all criteria throughout the admission period, the system dynamically adjusts the weighting factors based on the number of days since admission. This allows the prediction model to adapt to changing clinical conditions, where criteria importance evolves over time (e.g., acute symptoms on day 1 versus recovery patterns on later days), thereby improving prediction accuracy without requiring a completely separate model for each day.
Solution Approach 2:
The patent changes the parameters of the prediction system by introducing time-dependent weighting factors that modify the importance of different criteria based on admission duration. The weighting factors are adjusted as a function of days since admission, transforming the prediction model from a fixed-parameter system to one where parameters evolve over time. This enables the system to capture temporal patterns in patient deterioration or improvement while maintaining a unified prediction framework.
2Quantity of substance
If the prediction routine is run after the first day of admission, then more data is available for analysis, but the prediction becomes less accurate due to outdated weighting criteria
Solution Approach 1:
The system dynamically adjusts weighting factors based on the number of days since admission, allowing it to properly interpret accumulated data at different time points. On day 1, acute criteria are weighted heavily; on later days, the model shifts weight to reflect different clinical priorities such as recovery trends or persistent abnormalities. This dynamic reweighting ensures that increased data availability after day 1 enhances rather than degrades prediction accuracy.
Solution Approach 2:
The system incorporates feedback by continuously updating predictions as new daily data becomes available and recalculating with time-adjusted weighting factors. Each day's data is processed through the model with appropriate temporal weighting, allowing the system to learn from accumulated information while maintaining accuracy through time-sensitive criterion evaluation. The feedback loop ensures that more data leads to better predictions when processed with temporally appropriate weights.
3Measurement precision
If time-dependent weighting factors are implemented, then prediction accuracy for later admission days improves, but the computational complexity increases
Solution Approach 1:
The patent implements parameter changes by using time-dependent weighting factors that are calculated based on simple functions of admission duration. Rather than requiring complex machine learning models or extensive computational resources, the system adjusts weights using straightforward temporal parameters (days since admission). This approach maintains high automation levels while achieving improved accuracy, as the parameter changes are computationally efficient and can be implemented within existing prediction infrastructure.
Data Source
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AI summary
A method for predicting a patient's outcome variable, such as a probability of mortality/recovery, includes accessing at least one of a plurality past patients' data fields including physiological and/or laboratory data, and a time of stay indicating how long each patient had been under care at the time. An outcome variable estimation algorithm is generated by data mining from a plurality of past patients' physiological and/or laboratory data, corresponding time of stay, and associated outcome variables. A current patient's outcome variable is determined from the current patient's physiological and/or laboratory data, the current time of stay, and the outcome variable estimation algorithm.