Clinical Trial Data Retrieval Using AI Query-to-Schema Mapping
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Solution Overview
Problem
Accessing and analyzing clinical trial data is hindered by complex data formats, data segmentation across large repositories, and inconsistent data standards, leading to inefficiencies in retrieval and analysis.
Innovation Solution
Utilizing generative artificial intelligence (GenAI) systems and knowledge graphs to transform natural language queries into structured representations, which are then mapped to database schemas to generate database queries, with feedback loops for refinement, enabling efficient and customizable data retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to access and analyze clinical trial data, then data retrieval and analysis can be performed, but the process is hindered by complex data formats, data segmentation across large repositories, and inconsistent data standards, leading to inefficiencies and time consumption
Solution Approach 1:
The patent introduces a natural language processing system as an intermediary between users and complex clinical trial databases. This mediator automatically translates user questions into structured database queries, eliminating the need for users to manually navigate complex data formats and segmentation issues. The system handles data standardization and retrieval automation, significantly improving productivity while reducing time loss.
2Quantity of substance
If complex database schemas and segmented data repositories are used to store clinical trial data, then data organization and storage capacity are improved, but data access and retrieval become more difficult and time-consuming
Solution Approach 1:
The patent replaces manual mechanical operations of navigating complex database schemas with an automated natural language processing system. Instead of users manually constructing queries based on complex database structures, the system automatically translates natural language questions into appropriate database queries, making data access as easy as asking a question while maintaining the benefits of organized data storage.
3Adaptability or versatility
If manual data manipulation and merging of different data sets is required, then users can access information from multiple sources, but the process requires significant time and computational resources
Solution Approach 1:
The patent implements a self-service system where the natural language processing automatically handles data manipulation and merging tasks. When users ask questions requiring data from multiple sources, the system autonomously identifies, retrieves, and integrates the necessary data sets without requiring manual intervention, thereby maintaining adaptability across different data sources while dramatically improving processing efficiency.
4Stability of the object's composition
If multiple databases and dashboards are used to store clinical trial information, then data organization and categorization are improved, but users must know exactly where to find information, increasing complexity and time requirements
Solution Approach 1:
The patent creates a universal natural language interface that can access information across multiple databases and dashboards through a single entry point. Instead of requiring users to navigate different data structures and locations, the system provides a unified interface that automatically routes queries to the appropriate data sources, maintaining organized data storage while eliminating navigation complexity.
Data Source
AI summary
Methods and systems for retrieving data from at least one database based on a query input related to a clinical trial. A system processes a received query input related to a clinical trial using a pre-trained language model neural network. The neural network generates a structured representation of the query input. The system maps a first data field of the structured representation to a first column name and maps a second data field of the structured representation to a second column name. The system generates a database query based on (i) a database schema, (ii) the first column name, (iii) the second column name, (iv) data values associated with the first data field, and (v) data values associated with the second data field. The database query specifies an operation for joining data associated with the first column name with data associated with the second column name.