Medical Code Vector Embeddings for Automated Similarity Analysis
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Solution Overview
Problem
Current automated systems fail to effectively capture the complexity and similarity of medical information encoded using medical codes, as they lack the ability to interpret the relationships between medical codes due to the absence of grammar rules and require human knowledge for similarity determination.
Innovation Solution
Generating medical code vector embeddings using a medical embedding model trained with machine learning, which transforms medical codes into multi-dimensional vectors in a configurable space, allowing for the aggregation of vectors to determine similarities and differences between medical information sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If medical codes are used to encode medical information, then information can be standardized and processed automatically, but the system cannot capture similarity relationships between different codes without human knowledge
Solution Approach 1:
The patent introduces vector embeddings as an intermediary representation between discrete medical codes and similarity analysis. Each medical code is transformed into a multi-dimensional vector that captures semantic relationships, allowing automated systems to compute similarities through vector operations without requiring human medical knowledge.
Solution Approach 2:
The patent transforms one-dimensional discrete medical codes into multi-dimensional continuous vector spaces. This dimensional transformation enables the representation of similarity relationships by positioning codes with similar meanings closer together in the vector space, allowing automated detection of relationships that were invisible in the original code format.
2Measurement precision
If human medical knowledge is used to determine similarity between medical codes, then accurate similarity assessment is achieved, but the process requires manual intervention and cannot be fully automated
Solution Approach 1:
The patent enables the system to determine code similarities autonomously by training embedding models on medical data. The model learns similarity relationships from patterns in the data itself, allowing the system to self-assess similarities without requiring external human medical knowledge for each comparison.
Solution Approach 2:
The patent changes the representation parameters of medical codes from discrete alphanumeric strings to continuous multi-dimensional vectors. This parameter transformation allows numerical computation of similarities using standard vector operations, enabling automated precision measurement while maintaining accuracy through learned representations.
3Extent of automation
If vector embeddings are generated for medical codes, then automated similarity analysis becomes possible, but computational complexity and resource requirements increase
Solution Approach 1:
The patent performs embedding generation as a preliminary step that transforms medical codes into vector representations before analysis. Once codes are converted to vectors, similarity analysis becomes a straightforward computational operation, separating the complex transformation step from the simpler analysis step.
Solution Approach 2:
The patent creates vector copies of medical codes that preserve semantic information in a computationally convenient format. These vector representations serve as simplified proxies for the original codes, enabling efficient automated analysis without requiring complex rule-based systems.
Data Source
AI summary
Aggregate vectors corresponding to non-textual information/data are provided in a multi-dimensional space. A computing entity access a plurality of instances of medical information comprising medical codes. The computing entity generates one or more medical sentences from the plurality of instances of medical information. Each medical sentence comprises one or more medical codes. The computing entity generates an embedding vector dictionary comprising a plurality of multi-dimensional vectors based on a medical embedding model trained using machine learning and the one or more medical sentences. Each multi-dimensional vector corresponds to a medical code. The computing entity generates a plurality of aggregate vectors based on the embedding vector dictionary and analyzes at least a portion of the plurality of aggregate vectors to identify two or more aggregate vectors that are similar or different based on a distance between the two or more aggregate vectors in the multi-dimensional space.