LLM-Mediated Medical Database Queries From Natural Language
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Solution Overview
Problem
Users must manually enter criteria for medical database queries in a format determined by the user interface, which is inefficient and may not accurately reflect their intended search.
Innovation Solution
An apparatus and method that utilizes a large language model (LLM) to process natural language database queries, generating a medical database query through a feature set and a medical database query map, allowing for automated and accurate query generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually enter criteria for medical database queries in a format determined by the user interface, then the query can be executed, but the process is inefficient and may not accurately reflect the user's intended search
Solution Approach 1:
The patent replaces the mechanical manual input process with an automated LLM-based system. Users input natural language queries instead of manually filling structured forms, and the LLM automatically translates these natural language queries into proper database query formats, eliminating the need for users to understand or interact with complex user interface formats.
Solution Approach 2:
The LLM serves as an intermediary between the user's natural language query and the database query requirements. The system includes a query translator component that acts as a mediator, converting natural language into structured database queries without requiring users to directly interact with the database interface.
2Measurement precision
If users manually enter criteria for medical database queries, then the query format can be controlled by the user interface, but the query accuracy and precision may be compromised
Solution Approach 1:
The system replaces manual query entry with LLM-based automated query generation. The LLM processes natural language input and generates precise database queries, ensuring high query precision while abstracting away the complexity of the underlying database interface from the user.
Solution Approach 2:
The query translator performs self-service by automatically understanding and translating user intentions into precise database queries. The system independently analyzes natural language input, identifies relevant criteria, and generates accurate queries without requiring user guidance on format requirements.
3Productivity
If a large language model is used to process natural language queries and generate database queries, then query generation efficiency and accuracy improve, but the system complexity increases
Solution Approach 1:
The LLM is positioned as an intermediary service that handles the complex translation task between natural language and database queries. This separates the complexity of query generation from the core database functionality, allowing the database to remain simple while the LLM handles the sophisticated language processing.
Solution Approach 2:
The LLM-based query translator serves multiple functions: it processes natural language input, understands user intentions, translates queries into appropriate database syntax, and handles various query types. This multi-functionality consolidates what would otherwise require multiple separate systems into a single unified service.
Data Source
AI summary
Described herein is an apparatus and method for generating a medical database query. An apparatus may include at least a processor; and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to receive a first natural language database query; input the first natural language database query into a large language model (LLM); receive from the LLM a feature set; using a medical database query map, generate a first medical database query as a function of the feature set; and generate, using the LLM, an aggregated output by querying a medical database interfaced with the LLM using the first medical database query.