ML Weighted Medical Perspective Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing medical treatment recommendation systems fail to dynamically weight various medical perspectives based on a healthcare provider's learned preferences, leading to inconsistent and unpersonalized treatment recommendations.
Innovation Solution
A computing system that uses a machine learning model to assign weights to medical perspectives based on a healthcare provider's historical treatment selections, combining these weights with ranking scores from multiple medical perspectives to generate an overall score for each treatment option, thereby tailoring recommendations to the provider's preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple medical perspectives are used to evaluate treatment options, then the comprehensiveness of evaluation is improved, but the complexity of reconciling conflicting recommendations increases
Solution Approach 1:
The patent introduces an intermediary system that includes a machine learning model and aggregation logic to mediate between multiple medical perspectives. This intermediary automatically reconciles conflicting recommendations by weighting perspectives according to provider preferences and aggregating scores, thereby resolving conflicts without requiring manual intervention and reducing the perceived complexity for the end user.
Solution Approach 2:
The system dynamically changes the weighting parameters of different medical perspectives based on learned provider preferences. By adjusting these parameters automatically, the system adapts the evaluation process to individual provider needs while maintaining comprehensiveness across multiple perspectives, thus improving versatility without proportionally increasing complexity.
2Ease of operation
If existing systems rank treatment options separately based on isolated medical perspectives, then the simplicity of individual ranking is maintained, but the ability to provide personalized recommendations based on provider preferences is lost
Solution Approach 1:
The patent merges multiple isolated medical perspective rankings into a single aggregated recommendation by combining them through a weighted scoring system. The machine learning model integrates these separate rankings while incorporating provider preference data, thus maintaining the simplicity of individual perspective evaluation while achieving personalized recommendations through their combination.
Solution Approach 2:
The system incorporates feedback from provider historical selections to learn and adapt to provider preferences. This feedback mechanism allows the system to personalize recommendations over time by adjusting the weighting of different medical perspectives based on what each provider has historically selected, thereby achieving personalization while building upon the foundation of simple individual rankings.
3Reliability
If existing systems do not account for provider preferences in ranking medical perspectives, then the objectivity of the ranking is maintained, but the relevance of recommendations to individual providers is reduced
Solution Approach 1:
The system performs preliminary analysis of provider historical selections to establish preference profiles before generating treatment recommendations. By pre-processing this preference data and encoding it into weighting parameters, the system prepares the personalization framework in advance, ensuring that provider preferences are systematically incorporated without compromising the objectivity of the underlying medical evidence evaluation.
Solution Approach 2:
The weighting of medical perspectives transitions from static to dynamic, adapting to each provider's demonstrated preferences while maintaining objective medical evaluation. The system dynamically adjusts perspective weights based on learned provider characteristics, thereby preserving objectivity in the medical assessment while preventing loss of relevant provider-specific information through adaptive personalization.
Data Source
AI summary
A computing system receives a plurality of medical perspectives for each medical treatment option of a plurality of medical treatment options. A machine learning model assigns a weight to each medical perspective for each medical treatment option by determining how often a care provider has agreed with each perspective within the plurality of medical perspectives. The computing system receives a medical treatment option ranking for each of the medical perspectives. The computing system generates a score for each medical perspective as applied to each medical treatment option based on a combination of the weight and the ranking for each medical perspective. The computing system aggregates the scores across the plurality of medical perspectives into an overall score for each medical treatment option.