Neural Network Clinical Data Standardization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current knowledge management systems struggle to handle and standardize clinical data, particularly when it is scattered across different countries and languages, making it difficult to search and manage effectively for clinical trials.
Innovation Solution
A method and device utilizing a neural network model to refine and standardize multinational clinical data by outputting individual entity names, calculating similarities, and converting hierarchical database formats to relational formats, allowing for efficient and exact data standardization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing knowledge management systems are used to manage clinical data, then general knowledge management is achieved, but clinical data standardization and search efficiency deteriorate due to inability to handle multinational data variations
Solution Approach 1:
The patent segments clinical data into distinct entities (diseases, treatments, researchers, organizations) and applies separate standardization rules to each entity type. This segmentation allows the system to handle diverse multinational data formats while maintaining efficient search capabilities through entity-specific processing.
Solution Approach 2:
The patent introduces an intermediary standardization layer that translates various multinational clinical data formats into a unified standard format. This intermediary processing layer enables the system to accommodate diverse input formats while maintaining consistent output for efficient searching and analysis.
2Measurement precision
If detailed refinement and similarity calculation steps are added to the standardization process, then standardization accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by first extracting and refining entity names before conducting similarity calculations. This preliminary refinement step organizes the data in advance, making subsequent similarity calculations more efficient and accurate while reducing overall processing time through better data preparation.
Solution Approach 2:
The patent applies partial action by focusing similarity calculation only on refined entity names rather than entire data records. This selective approach maintains high standardization accuracy for critical fields while reducing unnecessary processing overhead on less important data elements.
Data Source
AI summary
A multinational clinical data standardization method according to an embodiment may include the steps of: outputting individual entity names using a neural network model from multinational clinical data; refining the individual entity names; calculating similarities for the refined individual entity names; and standardizing the multinational clinical data by reflecting the similarity calculation result.