Clinical Knowledge Database Integrating Structured and Unstructured Data
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Solution Overview
Problem
The challenge lies in efficiently retrieving and synthesizing information from diverse data sources, particularly the integration of structured and unstructured data, which often results in incomplete and inflexible information storage and retrieval due to the rigidity of structured data and the free-form nature of unstructured data, leading to inefficiencies in information retrieval systems.
Innovation Solution
A system and method that aligns structured data with unstructured data using natural language processing (NLP) models to generate a comprehensive knowledge database by extracting features, augmenting missing information, and performing sentiment analysis, allowing for efficient retrieval across multiple search parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structured data is used for information storage, then information retrieval specificity is improved, but data flexibility and completeness deteriorate
Solution Approach 1:
The patent merges structured data and unstructured data into a unified knowledge database system. The structured data provides predefined fields for specific retrieval, while unstructured data (natural language text) adds flexibility and comprehensive information coverage. The system processes both data types together, allowing queries to leverage the precision of structured data and the versatility of unstructured data simultaneously.
Solution Approach 2:
The knowledge database functions as a composite information structure, combining the rigid, organized nature of structured data with the flexible, comprehensive nature of unstructured data. This composite approach allows the system to maintain the advantages of both data types: the retrieval efficiency of structured data and the adaptability of unstructured data, creating a more robust information storage and retrieval system.
2Adaptability or versatility
If unstructured data is used for information storage, then data flexibility is improved, but information retrieval efficiency deteriorates
Solution Approach 1:
The system segments unstructured data into meaningful components through natural language processing. The unstructured text is divided into sentences, which are further processed to extract relevant features and entities. This segmentation allows the flexible unstructured data to be organized in a way that enables efficient retrieval, combining the versatility of unstructured storage with the efficiency of structured access patterns.
Solution Approach 2:
The patent introduces natural language processing models as an intermediary between unstructured data and retrieval operations. These models process unstructured text, extract meaningful features, and transform the data into a format that maintains flexibility while enabling efficient querying. The intermediary layer bridges the gap between the flexibility of unstructured data and the efficiency requirements of information retrieval.
3Loss of information
If both structured and unstructured data are integrated, then information completeness is improved, but system complexity increases
Solution Approach 1:
The knowledge database is designed as a universal system that handles both structured and unstructured data through a unified architecture. The same processing pipeline and storage mechanisms accommodate both data types, eliminating the need for separate specialized systems. This multi-functional approach achieves complete information integration while controlling complexity through standardized processing routines.
Solution Approach 2:
The system employs automated natural language processing models that self-manage the integration of structured and unstructured data. The NLP models automatically tokenize, extract entities, and align features without requiring complex manual intervention. This self-service capability reduces operational complexity while maintaining comprehensive information integration across both data types.
Data Source
AI summary
Systems and methods are described for a scalable approach to build a knowledge database of clinical trial data by extracting, aligning, and synthesizing information from a variety of sources including clinical trial registries, abstracts of papers, and full-text medical journal articles, as well as external gazetteers, dictionaries, and lexicons. For examples, a system may implement a flexible and repeatable workflow that extracts both structured and semi-structured elements from unstructured data such as journal articles using a ‘back off strategy’ in which specialized rules are used to extract structured, clinical trial design parameters as well as information retrieval techniques that exploit regularities in language used in the medical literature to discover semi-structured trial outcomes. This workflow also aligned structured elements with data from structured data sources and augmented the base structured information with additional searchable trial features or characteristics and sentiment or polarity scores derived from the unstructured data.