Event Sequence Management via Comparative Effectiveness Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for evaluating the effectiveness of event sequences in achieving a goal are inefficient and inaccurate, relying heavily on manual comparisons and group divisions, which are affected by individual patient factors and fail to account for joint effects of multiple events.
Innovation Solution
A method and system for identifying a candidate event sequence as effective by comparing its effectiveness with a reference sequence, where the candidate sequence includes at least one event not in the reference sequence, using historical data and hierarchical patterns to determine overall effectiveness without dividing patients into groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparisons and group divisions are used to evaluate event sequence effectiveness, then the evaluation process is simple to implement, but the measurement precision and accuracy are poor due to individual patient factors and inability to account for joint effects
Solution Approach 1:
The patent introduces a computational model as an intermediary between raw event sequence data and effectiveness evaluation. This model processes multiple event sequences and their joint effects systematically, eliminating the need for manual comparisons while accurately accounting for individual patient factors and combined event impacts.
Solution Approach 2:
The patent creates a virtual representation (computational model) of patient responses to event sequences. Instead of manually evaluating real patient data, the system uses copied/simulated data patterns to assess effectiveness, enabling precise measurement without direct manual intervention.
2Reliability
If traditional group division methods are used for evaluation, then the evaluation process is straightforward, but it fails to account for joint effects of multiple events and individual patient factors
Solution Approach 1:
The patent segments the evaluation process into distinct computational components: individual event effect analysis, joint effect calculation, and patient factor adjustment. This segmentation allows the system to reliably account for multiple factors simultaneously while maintaining evaluation efficiency through automated processing.
Solution Approach 2:
The patent transforms the evaluation from fixed group comparisons to dynamic parameter-based assessment. By changing parameters such as event sequence combinations and patient-specific factors, the system reliably evaluates effectiveness while efficiently processing multiple scenarios through computational variation.
Data Source
AI summary
A method, system, and computer program product for obtaining a candidate event sequence that includes at least one event for achieving a goal, obtaining a reference event sequence, the candidate event sequence comprising at least one event that is not comprised in the reference event sequence, comparing an effectiveness of the candidate event sequence on the goal and an effectiveness of the reference event sequence on the goal, and identifying the candidate event sequence as an effective sequence for achieving the goal in response to the effectiveness of the candidate event sequence being better than the effectiveness of the reference event sequence.