Clinical Trial Data Query Mapping for Faster Analysis
Find Innovative SolutionsGenerate Solutions
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, enabling efficient access and analysis of clinical trial data through database queries, with feedback loops for refinement.
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 inefficient and time-consuming due to complex data formats, data segmentation across large repositories, and inconsistent data standards
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user queries into database queries and maps results back to natural language. This intermediary system handles the complexity of data formats, segmentation, and inconsistent standards, allowing users to access clinical trial data efficiently without manually navigating complex repository structures
Solution Approach 2:
The system implements feedback loops where the AI model learns from user interactions and query patterns. By analyzing successful query transformations and user preferences, the system continuously improves its ability to retrieve relevant data faster, reducing both retrieval time and analysis time over successive iterations
2Loss of information
If complex database queries are executed to retrieve clinical trial data, then comprehensive data can be accessed, but computational resources and data transmission bandwidth are consumed
Solution Approach 1:
The system extracts only the specific data elements needed to answer the user's natural language query by translating it into targeted database queries. Rather than retrieving entire datasets, the AI identifies and extracts only relevant columns and rows, reducing computational resource consumption and data transmission bandwidth while maintaining data completeness for the specific information requested
Solution Approach 2:
The system performs partial action by retrieving slightly more data than strictly necessary to answer the query, then filtering and presenting only the relevant results. This approach accounts for potential data relationships and contextual information needs while avoiding the excessive resource consumption of retrieving complete datasets, balancing information completeness with resource efficiency
3Ease of operation
If users directly query complex database repositories without assistance, then data can be retrieved, but users need specialized knowledge of database schemas and data formats
Solution Approach 1:
The natural language processing system serves as an intermediary that shields users from database complexity. Users interact with the system using everyday language without needing to understand underlying database schemas, data formats, or query syntax. The intermediary handles all complexity of mapping natural language to database structures and back, making the system easy to operate despite the complexity of the underlying data repository
Solution Approach 2:
The system uses feedback from user interactions to continuously improve its understanding of user needs and refine its natural language processing capabilities. By learning from successful queries and user corrections, the system adapts to user preferences and patterns, progressively improving ease of operation while managing the inherent system complexity through intelligent adaptation
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.