Prediction Model for User Condition Risk Assessment
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Solution Overview
Problem
The growing amount of data generated daily leads to inefficiencies in sorting through stored data, with a significant portion being ignored or abandoned, which can result in undesirable service outcomes due to the time required for evaluation and the potential for missed insights.
Innovation Solution
A computer-implemented method using a prediction system that receives user data, inputs it into a trained prediction model to assess the risk of a user developing a condition, and provides recommendations based on this assessment, facilitating timely and informed decision-making in service management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is stored and evaluated manually, then service decisions can be made, but the time required creates inefficiencies and data may be ignored or abandoned
Solution Approach 1:
The system performs preliminary actions by pre-training prediction models with historical data and pre-calculating risk assessments. When a user record is evaluated, the model is already prepared to quickly process the data and generate predictions, eliminating the need for time-consuming manual analysis while maintaining decision accuracy
Solution Approach 2:
A prediction model acts as an intermediary between raw user data and service decisions. The model processes and interprets the data automatically, providing risk assessments that guide service decisions without requiring manual data evaluation, thus reducing time loss while preserving decision quality
2Measurement precision
If a prediction model is trained on training data, then risk assessment accuracy is improved, but the model complexity increases
Solution Approach 1:
The system optimizes model parameters through automated training processes, adjusting weights and thresholds to achieve high risk assessment accuracy. By systematically tuning parameters rather than manually designing complex model architectures, the system achieves precision while managing complexity through parameter optimization rather than structural complexity
Data Source
AI summary
Techniques are disclosed for determining a risk assessment corresponding to a level of risk that a user of a service organization has or will develop a condition. The techniques include receiving user data associated with a user record of a user. The techniques further include inputting the user data into a trained prediction model of a user condition prediction system. The user data may include a plurality of data points, weighted relative to each other, that respectively correspond to one of a plurality of categories. The techniques further include determining, by the trained prediction model, a risk assessment of a user condition based on the user data. The risk assessment may then be provided to a user device of a user service agent for use coordinating user service.