LLM-Based Health Data Standardization for Medical Analysis
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Solution Overview
Problem
Clinical and observational research studies face challenges in efficiently parsing and standardizing large datasets from diverse sources, due to varying syntactic standards, which hinders the identification of relationships and further analysis.
Innovation Solution
A system comprising a processor, multiple health databases with different syntactic standards, a phenotype routine database, and a computer-readable medium with instructions for data transformation, phenotype routine generation, and analysis to produce structured data and relationship models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple health databases with different syntactic standards is processed using traditional methods, then data parsing and standardization can be achieved, but the process becomes resource-intensive and time-intensive
Solution Approach 1:
The patent replaces traditional mechanical data parsing and standardization methods with a large language model (LLM) based natural language processing system. The LLM automatically transforms unstructured health data from multiple syntactic standards into structured data, eliminating the need for manual or rule-based parsing approaches while maintaining high accuracy in data standardization.
Solution Approach 2:
The system changes the operational parameters of data processing by using LLM-based semantic understanding instead of traditional syntactic rule matching. This parameter change allows the system to handle varying syntactic standards through contextual understanding rather than predefined transformation rules, significantly improving processing efficiency while maintaining standardization quality.
2Adaptability or versatility
If traditional data parsing methods are used to handle varying syntactic standards, then data can be processed, but the process requires significant computational resources and time
Solution Approach 1:
The patent substitutes traditional mechanical data parsing approaches with LLM-based natural language processing. The LLM's ability to understand and transform data across multiple syntactic standards through semantic comprehension dramatically reduces processing time compared to rule-based or manual methods, while maintaining high adaptability to varying data formats.
3Reliability
If manual or rule-based methods are used to parse and standardize health data, then data relationships can be identified, but the process is resource-intensive
Solution Approach 1:
The patent replaces resource-intensive manual or rule-based relationship identification methods with LLM-based semantic analysis. The LLM automatically identifies relationships among standardized health data through contextual understanding, significantly reducing computational resource consumption while maintaining or improving the accuracy of relationship identification.
Data Source
AI summary
A system includes a processor and a nontransitory computer-readable medium comprising instructions that are executable by the processor. The instructions include receiving data from at least one health database, performing a large language model (LLM) routine to transform the data into structured data, generating a phenotype routine by selectively modifying at least one parameter of a selected template phenotype routine of the plurality of template phenotype routines, analyzing the structured data based on the phenotype routine to generate an output that defines a relationship model associated with the structured data, and transmitting a command to a user interface to generate a display corresponding to the output.