Differential Periodic ML Scoring for Sparse Time-Series Evaluations
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Solution Overview
Problem
Existing machine learning frameworks for timeseries/periodic data are inadequate for comprehensive predictive analysis due to insufficient data for shorter periods, leading to inefficiencies and inaccuracies in determining parameters for longer periods, particularly in healthcare provider engagement and reimbursement systems.
Innovation Solution
A machine learning framework with a first periodic tier for single evaluation periods and a second tier for multiple periods, using computationally efficient linear regression for single periods and complex processing for multiple periods, to determine predicted evaluation scores, incorporating engagement attributes and cross-platform deviation measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning framework is used for both single and multiple evaluation periods, then the system is simple to implement, but the accuracy of predictive analysis is insufficient due to inadequate data for shorter periods
Solution Approach 1:
The patent divides the evaluation system into two distinct tiers: a first periodic tier for single evaluation periods and a second periodic tier for multiple evaluation periods. This segmentation allows each tier to be optimized independently, with the first tier handling short-term evaluations and the second tier handling long-term evaluations, thereby resolving the accuracy issue while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces a temporal dimension by creating separate evaluation tiers for different time horizons (single period vs. multiple periods). This dimensional approach allows the system to process evaluations of varying durations appropriately, improving predictive accuracy by matching the evaluation framework to the specific time horizon rather than using a one-size-fits-all approach.
2Measurement precision
If comprehensive processing is applied to all predictive entities, then the accuracy of long-term predictions improves, but the computational efficiency decreases for short-term evaluations
Solution Approach 1:
The patent segments the processing logic into two distinct pathways: the first periodic tier for single evaluation periods uses streamlined processing optimized for speed and efficiency, while the second periodic tier for multiple evaluation periods applies comprehensive complex processing optimized for accuracy. This segmentation ensures that computational resources are appropriately allocated based on the evaluation requirements.
Solution Approach 2:
The patent applies different processing qualities to different evaluation scenarios: simple, efficient processing for single-period evaluations and complex, comprehensive processing for multi-period evaluations. This local quality approach ensures that each evaluation type receives the appropriate level of processing detail, optimizing both computational efficiency and prediction accuracy for each specific use case.
3Reliability
If data from multiple evaluation periods is aggregated, then the reliability of long-term predictions improves, but the system becomes more complex in handling cross-platform deviations
Solution Approach 1:
The patent extracts and isolates the cross-platform deviation analysis as a distinct component within the second periodic tier. By separating the deviation detection and penalty application from the core evaluation logic, the system manages the complexity of handling multi-period data while maintaining improved prediction reliability through comprehensive analysis.
Solution Approach 2:
The patent introduces cross-platform deviation measures and penalties as intermediary elements that mediate between raw evaluation data and final predictions. These intermediaries systematically handle the complexity of aggregating data across multiple periods and platforms, transforming raw multi-source data into reliable predictions through structured deviation analysis and penalty mechanisms.
Data Source
AI summary
Various embodiments of the present invention address technical challenges associated with performing machine learning operations on timeseries/periodic data by introducing a machine learning framework that has a first periodic tier for determining predicted evaluation scores for those predictive entities that are associated with a single evaluation period (e.g., a single year of data) and a second periodic tier for determining predicted evaluation scores for those predictive entities that are associated with multiple evaluation periods. The noted framework addresses the existing shortcomings of machine learning frameworks that operate on timeseries/periodic data with respect to inadequacy of data associated with shorter periods to determine parameters needed to perform comprehensive predictive data analysis with respect to longer periods.