Entity Differentiation Algorithms for Medical Concept Sorting
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Solution Overview
Problem
Current medical diagnosis systems face challenges in automatically processing medical text to select the most reliable attribute values for medical concepts, leading to conflicts and delays in treatment recommendations due to the need for human clinicians to resolve discrepancies among multiple annotations.
Innovation Solution
A cognitive medical system employing machine learning-based entity differentiation algorithms to rank and select the most reliable attribute values for medical entities, using a framework that learns the order and combination of algorithms to prioritize annotations based on context and relevance, thereby facilitating accurate treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple entity differentiation algorithms are applied to rank medical entity attribute values, then the reliability of selected attribute values is improved, but the device complexity increases
Solution Approach 1:
The system segments the entity differentiation process into multiple independent algorithms, each handling specific aspects of attribute value ranking. These algorithms are applied in a determined order to progressively refine the ranking, allowing the system to manage complexity through modular decomposition while maintaining high reliability through comprehensive multi-algorithm analysis.
2Productivity
If automated entity differentiation is implemented, then the productivity of treatment recommendation is improved, but the measurement precision of attribute value selection may worsen
Solution Approach 1:
The system incorporates feedback mechanisms where the output of each differentiation algorithm informs the input and parameters of subsequent algorithms. This feedback loop allows the automated system to continuously refine attribute value rankings based on intermediate results, maintaining measurement precision while achieving high productivity through automated iterative processing.
3Manufacturing precision
If a large number of entity differentiation algorithms are executed in order, then the manufacturing precision of ranked attribute values is improved, but the loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-determining the optimal order of algorithm execution based on the specific medical entity and attribute being analyzed. This preliminary planning allows the system to execute algorithms in the most efficient sequence, reducing unnecessary computation time while maintaining manufacturing precision through systematic application of all required algorithms.
Data Source
AI summary
Mechanisms are provided for performing entity differentiation. A cognitive medical system ingests a corpus of medical content having references to medical entities, and performs entity recognition on the medical content to identify the medical entities. Responsive to the cognitive medical system identifying a medical entity having a plurality of annotations for a same medical entity attribute, an entity differentiation component executes an ordered set of entity differentiation algorithms, corresponding to the medical entity, for differentiating medical entity attribute values. The entity differentiation component runs the ordered set of entity differentiation algorithms, in order, on the plurality of annotations for the attribute to generate a ranked list of medical entity attribute values corresponding to the annotations in the plurality of annotations. The cognitive medical system performs a cognitive operation on the medical entity based on the ranked list of medical entity attribute values.