Clinical Intervention Evaluation via User-Prioritized Pairwise Comparisons
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Solution Overview
Problem
Current methods for evaluating net treatment benefits in clinical data lack flexibility and do not adequately consider personalized preferences, leading to a need for improved statistical analysis approaches that incorporate user inputs for granular inferences.
Innovation Solution
A computer-implemented method for identifying net treatment benefits by obtaining datasets for treatment and reference subjects, performing pairwise comparisons based on user-provided prioritization functions, and determining the net benefit of clinical interventions, which can include various treatments and outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional statistical analysis methods are used to evaluate treatment benefits, then the analysis is simple and fast, but it lacks flexibility and cannot adequately consider personalized preferences
Solution Approach 1:
The patent segments the treatment evaluation process into multiple components: obtaining treatment outcomes, obtaining reference outcomes, receiving user prioritization input, performing pairwise comparisons, and determining net treatment benefit. This segmentation allows each component to be optimized independently while maintaining overall flexibility.
Solution Approach 2:
The patent introduces dynamic user prioritization input that allows clinicians and patients to adjust the relative importance of different treatment outcomes based on individual preferences. This dynamic adjustment capability enables the system to adapt to personalized needs without requiring a complete redesign of the analysis framework.
2Measurement precision
If personalized preferences are incorporated into treatment evaluation, then the evaluation becomes more accurate and meaningful, but the complexity of the analysis increases
Solution Approach 1:
The patent performs preliminary pairwise comparisons between treatment and reference outcomes before final evaluation. By pre-organizing the data and performing initial comparisons, the system reduces the complexity of the final net benefit calculation while maintaining high precision through user-informed prioritization.
Solution Approach 2:
The patent introduces an intermediary prioritization function that mediates between raw treatment outcomes and final net benefit determination. This intermediary layer translates complex multi-criteria data into a structured format that can be efficiently processed while preserving the nuance of personalized preferences.
3Loss of information
If pairwise comparisons are performed between treatment and reference subjects, then granular inferences can be made, but the computational workload increases
Solution Approach 1:
The patent performs pairwise comparisons selectively based on user prioritization rather than exhaustively comparing all possible outcome pairs. By focusing computational resources on the most relevant comparisons according to user preferences, the system maintains high information retention while improving evaluation speed.
Data Source
AI summary
In an aspect, the present disclosure provides a method for identifying a net treatment benefit of a clinical intervention for a subject, comprising: obtaining a dataset for a treatment set of subjects and a reference set of subjects; obtaining a plurality of treatment outcomes for the treatment set of subjects and the reference set of subjects; receiving user input of a prioritization function of the plurality of treatment outcomes; performing a set of pairwise comparisons between a first subject selected from the treatment set of subjects and a second subject selected from the reference set of subjects; and determining the net treatment benefit of the clinical intervention for the subject.