Clinical Trial Data Embedding for Similar Study Retrieval
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Solution Overview
Problem
Existing paper-based clinical trial data management systems face challenges in data storage, security, data sharing, flexibility, and restricted utilization, limiting the efficiency and effectiveness of clinical trial data management and analysis.
Innovation Solution
A system that classifies metadata and natural language data from clinical trial data, generates tokens, and uses embedding vectors to recommend similar clinical trial data through a feature extractor and data recommender, employing one-hot encoding, matrix factorization, and ensemble models to calculate distances between vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If paper-based clinical trial data management is used, then data storage and maintenance are simple, but data sharing, reprocessing, flexibility, and utilization are extremely restricted
Solution Approach 1:
The patent replaces the mechanical paper-based system with an electronic data management system that uses embedding vectors and machine learning models to enable advanced data processing, sharing, and utilization capabilities while maintaining systematic organization
Solution Approach 2:
The patent transforms clinical trial data from unstructured paper formats into structured electronic data representations through embedding vectors, enabling the data to be processed, searched, and utilized in fundamentally new ways while maintaining its informational integrity
2Reliability
If paper-based management is used, then system implementation is simple, but data security and storage reliability are extremely vulnerable
Solution Approach 1:
The patent substitutes the vulnerable paper-based storage system with a secure electronic data management platform that provides enhanced data security, reliable storage, and protected access through digital infrastructure
Solution Approach 2:
The patent creates digital copies of clinical trial data in the form of embedding vectors that preserve the essential information while enabling secure storage, sharing, and processing without the vulnerabilities of physical paper documents
3Productivity
If electronic data-based management is implemented, then data sharing and utilization are improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary processing by converting clinical trial data into embedding vectors and storing them in advance, enabling fast retrieval and processing when needed without the complexity of processing raw data at the moment of use
Solution Approach 2:
The patent introduces embedding vectors as an intermediary representation that bridges the gap between raw clinical trial data and processing needs, simplifying data processing operations while maintaining the full information content
Data Source
AI summary
Disclosed are an apparatus and a method for recommending similar clinical trial data to extract clinical trial data similar to clinical trial data which is input by a user. A similar clinical trial data recommending apparatus according to an exemplary embodiment may include a preprocessor which classifies metadata and natural language data included in clinical trial data and generates a token for the natural language data; a feature extractor which generates an embedding vector based on the metadata and the token; and a data recommender which extracts one or more similar clinical trial data within a predetermined distance, among one or more previously stored clinical trial data, based on a distance between an embedding vector generated from input clinical trial data which is requested to be searched by a user and an embedding vector generated from one or more previously stored clinical trial data.