Disability Income Recovery Prediction Using Gradient Boosting
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Solution Overview
Problem
Conventional computer-based systems for managing disability insurance claims struggle to accurately predict the return-to-work timeframe for employees on disability leave, as the likelihood of returning to work decreases over time, and claims examiners face challenges in tracking and prioritizing claimants due to resource constraints and laborious manual procedures.
Innovation Solution
A predictive machine learning model using discrete-time survival analysis and gradient boosting is employed to analyze disability income claim data, providing ranked claimant records and recovery scores, which are displayed through a graphical user interface, enabling claims examiners to identify claimants likely to return to work and update information in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer-based systems are used to manage disability insurance claims, then claims can be processed, but the systems cannot accurately predict return-to-work timeframe as likelihood decreases over time
Solution Approach 1:
The patent replaces manual tracking methods with an automated computer-based system that uses machine learning algorithms to predict return-to-work timeframes. The system automatically monitors claimant data and updates predictions over time, substituting the mechanical manual processes with intelligent automated systems that can handle the complexity of predicting variable recovery timelines.
Solution Approach 2:
The system enables claims examiners to independently access and update claimant information through a graphical user interface. The machine learning model automatically recalculates predictions based on new data without requiring manual intervention, allowing the system to serve itself in maintaining accurate predictions while reducing the time burden on examiners.
2Reliability
If claims examiners manually track all claimants, then comprehensive monitoring is possible, but resource constraints and laborious procedures make this impractical
Solution Approach 1:
The patent replaces manual tracking procedures with an automated computer-based system that continuously monitors claimant status. The system uses machine learning algorithms to automatically process and analyze claimant data, substituting the mechanical manual tracking process with an efficient automated system that maintains reliable monitoring without requiring extensive examiner resources.
Solution Approach 2:
The system introduces a computer-based intermediary that acts as a bridge between claims examiners and claimant data. This intermediary automatically collects, processes, and analyzes claimant information, then presents relevant insights to examiners through a graphical user interface, reducing the direct labor burden while maintaining comprehensive tracking reliability.
3Loss of information
If the system provides detailed analysis of all claimants, then comprehensive insights are available, but the complexity of processing and presenting data increases
Solution Approach 1:
The patent extracts and presents only the most relevant information to claims examiners through the graphical user interface, rather than displaying all available data. The machine learning model identifies and highlights key predictors and risk factors for each claimant, extracting essential insights from the complex dataset while filtering out unnecessary details, thus maintaining information completeness without overwhelming complexity.
Solution Approach 2:
The system applies different levels of detail and analysis to different claimants based on their individual characteristics and risk profiles. High-risk claimants receive more detailed analysis and monitoring, while lower-risk claimants receive streamlined information, allowing the system to maintain comprehensive information availability while adapting the complexity of data presentation to local needs.
Data Source
AI summary
A DI recovery management system generates a plurality of ranked claimant records and recovery scores. A predictive machine learning model inputs disability income claim data and disability income claimant data into an event history model utilizing discrete-time survival analysis in conjunction with a gradient boosting machine learning model. The claim termination event is one of a plurality of preselected recovery events that indicate that a claimant has achieved return-to-work capacity. Claimant data used in modeling includes diagnosis data representative of workplace disability duration guidelines. The predictive machine learning model is continually trained using updated disability income claims data. The training procedure transforms claimant records extracted from a DI claims database into a longitudinal format that includes multiple person-year records corresponding to each claimant record. A DI recovery dashboard displays a hazard plot representing a conditional probability over time that a claimant will realize a claim termination event.