Log-Expit Transformation for Influenza Risk Prediction
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Solution Overview
Problem
Current methods lack accurate predictive models and sufficient data for determining aggregate loss risk associated with pandemic influenza, particularly for health insurers and reinsurers, due to uncertainty in virus characteristics and lack of detailed data for extreme-valued, high-severity claims.
Innovation Solution
A system and method using log-expit transformation and non-parametric gradient-boosting machine-learning modeling to predict aggregate loss statistical distributions based on historical insurance claims and electronic health record information, enabling the securitization of epidemic or pandemic acute-care health services catastrophe risk through the issuance of risk instruments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical methods are used to estimate influenza loss risk, then the approach is simple and data requirements are low, but the prediction accuracy for extreme-valued high-severity claims is insufficient
Solution Approach 1:
The patent transforms the raw claims data using log-expit transformation to change the parameter space, enabling better modeling of extreme-valued claims. This transformation converts the skewed distribution of healthcare costs into a more manageable form that gradient-boosting models can process effectively, improving prediction accuracy for high-severity claims while maintaining computational feasibility
Solution Approach 2:
The patent replaces traditional statistical mechanical methods with machine learning-based gradient-boosting models. This substitution allows the system to capture complex non-linear relationships in healthcare cost data that traditional statistical methods miss, significantly improving prediction accuracy for extreme events despite increased computational requirements
2Measurement precision
If detailed historical data is collected to improve risk prediction accuracy, then the measurement precision improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent creates a multi-functional data collection system that gathers information serving multiple purposes: clinical outcomes, cost analysis, utilization patterns, and risk prediction. By designing the data collection to serve multiple functions simultaneously, the system reduces overall difficulty despite the extensive data requirements needed for accurate extreme-event prediction
3Reliability
If reinsurance and catastrophe bonds are issued to mitigate aggregate loss risk, then the financial stability improves, but the device complexity increases
Solution Approach 1:
The patent segments aggregate influenza risk into individual hospitalization claims and groups them into manageable units for reinsurance and catastrophe bond structures. This segmentation allows insurers to transfer specific portions of risk (e.g., claims exceeding certain thresholds) to reinsurers and capital markets, improving financial stability while maintaining manageable complexity through modular risk transfer mechanisms
Data Source
AI summary
Systems, methods and computer-readable media are provided for determining and mitigating the aggregate loss risk associated with hospitalization for epidemic or pandemic influenza for health insurers, reinsurers, provider organizations, or public policy-makers. An accurate prediction of this risk may be provided, which may be used to determine parameters for reinsurance underwriting or for issuance and trading of catastrophe bonds (“cat bonds”) or other insurance-linked securities (ILS) and derivatives to lay off substantial amounts of such risk to capital markets investors. In particular, one embodiment uses a novel log-expit transformation of the raw data and non-parametric gradient-boosting machine-learning modeling in order to determine a high-claim right-tail risk. Some embodiments further comprise securitizing epidemic or pandemic influenza acute care health services catastrophe risk.