Multi-Stage Machine Learning for Readmission Prediction
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Solution Overview
Problem
Conventional techniques for predicting and mitigating preventable resource utilization due to readmission of an entity to a resource system are inadequate as they rely on datasets available at a specific point in time, failing to consider changes in an entity's need for readmission over time and under varying circumstances.
Innovation Solution
A computer-implemented method that uses a multi-stage approach by applying multiple machine learning models trained with data from different stages of an entity's interaction with a resource system, allowing for the updating of prediction values and the initiation of mitigation actions based on evolving data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques use datasets available at a specific point in time for prediction, then the prediction process is simple and quick, but the prediction accuracy deteriorates because it fails to consider changes in entity's need for readmission over time
Solution Approach 1:
The patent divides the prediction system into multiple stage-specific machine learning models (first stage model, second stage model, etc.) that process data at different time points. Each model is trained on features available during its specific stage, allowing the system to capture temporal changes in readmission risk while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent implements a dynamic prediction approach where the system updates prediction values as new data becomes available at each stage. The multi-stage modeling framework allows predictions to evolve over time, adapting to changing entity conditions and circumstances rather than relying on static single-point-in-time assessments.
2Adaptability or versatility
If conventional techniques rely on a single model or algorithm for prediction, then the system is simple to implement, but it fails to capture evolving readmission risks under varying circumstances
Solution Approach 1:
The patent segments the prediction task into multiple stage-specific models, where each model is specialized for predicting readmission risk at a particular stage of entity interaction with the resource system. This segmentation allows each model to be optimized for stage-specific data characteristics while collectively providing comprehensive risk assessment across all stages.
Solution Approach 2:
The multi-stage modeling framework serves multiple functions: it captures temporal evolution of risk, adapts to varying circumstances at different stages, and provides a structured approach to integrating diverse data sources. The framework is universally applicable to different resource systems and entity types while maintaining flexibility for stage-specific customization.
3Reliability
If conventional techniques collect and analyze limited data for prediction, then the data processing is efficient and quick, but the prediction reliability deteriorates due to insufficient information about future changes
Solution Approach 1:
The patent applies preliminary action by training stage-specific machine learning models in advance, with each model pre-trained on features available during its corresponding stage. This preliminary training allows the system to quickly process new data at each stage without extensive real-time computation, improving reliability through pre-established predictive capabilities while minimizing processing delays.
Solution Approach 2:
The patent ensures continuous useful action by systematically collecting and analyzing data at each stage as it becomes available, rather than waiting for complete datasets. The multi-stage approach maintains continuous prediction updates, ensuring that reliable predictions are generated at each point in time using the best available information up to that stage.
Data Source
AI summary
A method includes: receiving a first set of data associated with an element during a first stage of a plurality of stages; applying a first stage machine learning model to the first set of data to generate a prediction value, wherein the first stage machine learning model is trained with a first set of feature data that is available during the first stage; updating the prediction value by: receiving a second set of data associated with the element during a second stage of the plurality of stages; and applying a second stage machine learning model to the second set of data, wherein the second stage machine learning model is trained with (i) the first set of feature data, and (ii) a second set of feature data that is available during the second stage; and initiating performance of a mitigation action based on the updated prediction value.