Hierarchical Intervention ML Framework for Risk Scoring
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Solution Overview
Problem
Current predictive data analysis systems face challenges in efficiently performing real-time operations due to high computational loads and resource requirements, particularly in generating risk scores for intervention recommendations, which can impact operational reliability and efficiency.
Innovation Solution
A hierarchical intervention recommendation machine learning framework is introduced, which limits real-time computational operations to generate intermediate intervention scores, postponing final risk score determination until after intermediate interventions. This framework includes real-time risk scoring, intermediate risk scoring, and risk aggregation models to optimize computational efficiency and reduce resource needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time risk scoring operations are performed continuously, then operational reliability is improved, but computational load and resource requirements increase
Solution Approach 1:
The risk scoring process is segmented into distinct phases: real-time risk scoring using sensory data, intermediate risk scoring after interventions, and final risk aggregation. This segmentation allows the system to perform computational operations at appropriate intervals rather than continuously, reducing overall computational load while maintaining operational reliability through staged assessments.
Solution Approach 2:
The system performs preliminary real-time risk scoring operations using available sensory data before interventions occur. This preliminary assessment establishes a baseline risk level that can be compared against intermediate and final risk scores, allowing the system to maintain operational reliability without requiring continuous full-scale scoring operations.
2Reliability
If final risk score determination is performed in real-time, then operational reliability is improved, but real-time operational load increases
Solution Approach 1:
The final risk score determination is segmented into multiple stages: real-time preliminary scoring, intermediate scoring after interventions, and final aggregation. This segmentation moves the computationally intensive final aggregation operation out of the real-time critical path, improving real-time operational efficiency while maintaining reliability through the staged assessment approach.
Solution Approach 2:
The system performs preliminary risk assessments in real-time using available data, then postpones the final risk score determination until after interventions are completed. This preliminary action provides timely risk information without requiring the full computational burden of final score determination during critical real-time operations.
3Measurement precision
If extensive training and storage resources are allocated, then measurement precision is improved, but resource requirements increase
Solution Approach 1:
The risk assessment is segmented into multiple scoring operations that use different data inputs and models. This segmentation allows the system to achieve precise risk measurements through cumulative assessment rather than requiring a single comprehensive model that would demand extensive training and storage resources.
Solution Approach 2:
The system performs partial risk assessments at intermediate stages using available data, rather than requiring complete data sets for full precision scoring. This partial action approach maintains adequate measurement precision for operational decisions without allocating resources for exhaustive data collection and processing.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations using a hierarchical intervention recommendation machine learning framework. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations using at least one of the techniques using real-time sensory timeseries data object, techniques using global baseline sensory feature data object, techniques using intermediate intervention operations, techniques using real-time risk scores, techniques using intermediate risk scores, and/or the like.