Multi-event-type prediction model with incidence rate penalty weights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing prediction systems face inefficiencies and inaccuracies in performing multi-event-type predictive data analysis, particularly in handling rare events and requiring multiple training sessions for per-event-type models, which increases computational and storage costs.
Innovation Solution
The development of a single multi-event-type prediction model that uses per-event-type and cross-event-type loss values, incorporating incidence rate penalty weights to adjust loss values, allowing for efficient training and improved prediction accuracy across multiple event types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple per-event-type prediction models are trained separately, then prediction accuracy for each event type may be improved, but computational costs and storage requirements increase significantly
Solution Approach 1:
The patent combines multiple per-event-type prediction models into a single multi-event-type prediction model that processes multiple event types simultaneously. This merging approach reduces the total computational overhead and storage requirements while maintaining prediction accuracy through a unified model architecture that handles diverse event types in one training session.
Solution Approach 2:
The patent creates a universal prediction model that can handle multiple event types through a single model instance. This multi-functional model uses event-type identifiers and prospective timeframes to adaptively predict different event types without requiring separate specialized models for each event, thereby reducing computational and storage costs.
2Measurement precision
If multiple per-event-type prediction models are trained separately, then prediction accuracy for each event type may be improved, but storage requirements increase due to multiple model instances
Solution Approach 1:
The patent merges multiple event-type specific models into one consolidated multi-event-type model. This single model stores shared parameters and event-type specific parameters in a unified structure, significantly reducing the total storage footprint compared to maintaining separate model instances for each event type while preserving the ability to accurately predict each event type.
3Adaptability or versatility
If multiple training sessions are conducted for per-event-type models, then comprehensive coverage of different event types is achieved, but training time and computational resources are multiplied
Solution Approach 1:
The patent combines multiple training sessions into a single training process that handles multiple event types simultaneously. The unified model is trained on diverse training data spanning multiple event types in one session, using event-type identifiers to differentiate between event types during training, thereby achieving comprehensive event type coverage without multiplying training time.
Solution Approach 2:
The patent incorporates event-type identifiers and prospective timeframes into the training data structure from the beginning, allowing the model to learn event-type specific patterns and temporal relationships in advance during a single training session. This preliminary structuring of training data enables the model to adapt to multiple event types without requiring sequential training sessions.
4Reliability
If repeat-occurrence training data fields are included in training, then model robustness may be improved, but prediction accuracy for first-occurrence events deteriorates due to data contamination
Solution Approach 1:
The patent segments the training data into distinct subsets based on event occurrence type (first-occurrence vs. repeat-occurrence). By filtering and separately processing these data segments, the model learns to distinguish between first-occurrence and repeat-occurrence patterns without contamination, improving first-occurrence prediction accuracy while maintaining robustness through comprehensive training on both data types.
Data Source
AI summary
There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.