AI Training Data Query Generation for Healthcare Scarcity
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Solution Overview
Problem
Existing AI training data collection methods face challenges in efficiently gathering specific data items for healthcare AI models, particularly for rare diseases, due to limited data availability and the need for high accuracy, which complicates the training process.
Innovation Solution
An AI training data creation support system that includes a processor and storage device to receive user inputs, calculate the required data quantity, and generate supplementary queries to extract necessary data from databases, ensuring sufficient data for model training while protecting privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data collection methods are used for healthcare AI models, then data privacy is protected, but data collection efficiency and completeness are insufficient
Solution Approach 1:
The system segments the data collection process into multiple independent query components. Each query targets specific data items or conditions, allowing systematic extraction of comprehensive training data while maintaining privacy through controlled access to individual data segments rather than bulk data exposure
Solution Approach 2:
The system introduces an intermediary data collection mechanism that acts between the user and the database. This intermediary automatically generates and executes multiple queries based on user specifications, completing data collection tasks efficiently while the system maintains privacy protection through controlled data access and processing
2Measurement precision
If multiple specific data items are required for AI training, then model accuracy is improved, but data collection complexity increases
Solution Approach 1:
The system provides a universal data collection interface that handles multiple data items and query types through a single unified mechanism. The query generation and execution system is designed to work with various data structures and conditions, reducing complexity by providing a consistent approach regardless of the specific data requirements
Solution Approach 2:
The system performs preliminary actions by automatically generating appropriate queries and executing data extraction operations before the user needs the data. The system proactively collects and prepares training data based on user specifications, eliminating the need for users to manually construct and execute complex queries for each data item
3Adaptability or versatility
If data from rare diseases is collected for AI training, then healthcare model applicability is improved, but data availability is limited
Solution Approach 1:
The system applies partial action by collecting all available data for rare diseases without requiring excessive or unrealistic data quantities. The query system extracts every relevant record that exists in the database, accepting that the quantity may be limited but ensuring complete utilization of available rare disease data for training model applicability to underserved conditions
Data Source
AI summary
To efficiently collect training data for training an AI model, an input of a training profile is received that includes item values corresponding to a plurality of data items, including analysis target data to be analyzed by the AI model and information on the model type. A first query is acquired to extract training data from a training database. The number of pieces of first training data to be extracted from the training database is calculated. The required number of pieces of the training data to train the AI model is calculated using the information on the model type. Whether the number of pieces of the first training data is equal to or greater than the required number is determined. When the determined number of pieces of the first training data is less than the required number, a supplementary query for extracting the training data is generated.