LLM Concept Mapping for Ambiguous Healthcare Codes
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Solution Overview
Problem
Healthcare data systems face challenges due to ambiguous textual representations in the form of synonyms, acronyms, and abbreviations across different code sets, leading to semantic variance and hindered data interoperability.
Innovation Solution
A system utilizing large language models, such as SAPBERT, generates vector embeddings for mapped and unmapped proprietary and standard codes, calculating similarity measures to recommend candidate standard codes for mapping, enhancing data interoperability through precise code associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional code mapping methods are used, then data interoperability is maintained at basic levels, but semantic variance and ambiguity in code representations hinder accurate data exchange
Solution Approach 1:
The patent introduces large language models as intermediary components that bridge proprietary code systems and standard code systems. The LLM processes ambiguous textual representations (synonyms, acronyms, abbreviations) and generates accurate mappings to standard codes, serving as a mediator that resolves semantic variance without requiring direct complex interactions between different code systems
Solution Approach 2:
The patent transforms code mapping from traditional exact-match parameter comparison to semantic similarity-based parameter matching. By using LLM-generated vector embeddings and similarity thresholds, the system can accommodate semantic variance in code representations while maintaining mapping accuracy, effectively changing the parameters used for code comparison and matching
2Adaptability or versatility
If multiple code sets with synonyms and abbreviations are used, then data flexibility and adaptability are improved, but code ambiguity increases and interoperability suffers
Solution Approach 1:
The patent creates a standardized representation copy of ambiguous code representations through LLM-generated standard code mappings. Instead of working with the original ambiguous textual representations directly, the system generates standardized code copies that preserve the semantic meaning while eliminating ambiguity, allowing flexible data representation without loss of information
Solution Approach 2:
The patent performs preliminary disambiguation of code representations before data exchange occurs. By pre-processing ambiguous textual representations through LLM analysis and generating standardized code mappings in advance, the system resolves code ambiguity before it can cause interoperability issues, maintaining both flexibility and clarity
3Measurement precision
If manual code mapping is performed, then mapping accuracy can be controlled, but processing time and system complexity increase
Solution Approach 1:
The patent implements self-service code mapping where the LLM autonomously processes code mappings without requiring manual intervention. The system automatically generates vector embeddings, calculates similarities, and produces standard code mappings independently, maintaining high accuracy while significantly reducing processing time compared to manual methods
Solution Approach 2:
The patent replaces manual mechanical code mapping processes with automated LLM-based computational processes. Instead of human operators manually comparing and mapping codes, the system uses LLM-generated vector embeddings and similarity calculations to automatically perform mappings, maintaining accuracy while eliminating time-consuming manual operations
Data Source
AI summary
Techniques for generating recommendations of candidate standard codes for association with unmapped proprietary codes are disclosed. Initially, the system generates vector embeddings for mapped standard codes by applying a vector embedding function to datasets of proprietary codes that are mapped to the respective mapped standard codes. The system generates vector embeddings for unmapped standard codes by applying a vector embedding function to a dataset of the unmapped standard codes. The system compares a target vector embedding for a target unmapped proprietary code to the vector embeddings computed for each of the mapped and unmapped standard codes. Based on a similarity measure between the target vector embedding and the vector embeddings for the mapped and unmapped standard codes, the system selects a subset of the mapped and unmapped standard codes for recommending to the user as a set of candidate standard codes for mapping to the target unmapped proprietary code.