Medical Data Testing via Standardized Pattern Matching
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Solution Overview
Problem
Existing methods for processing medical data are time-consuming and costly due to the need for structured data unification across multiple terminals with different requirements, especially in scenarios with uneven data quality, hindering the development of medical artificial intelligence.
Innovation Solution
A method for quickly testing medical data by matching it against a standardized library using a specific pattern expression that assesses similarities in non-initial and initial boundaries, information unit quantities, sequences, and semantic relationships, without requiring word segmentation, to determine data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data interaction theories are used to implement strong logicality on interactions of multiple terminals, then data structure unification is achieved, but processing time and costs increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining standardized data structures and interaction protocols before actual data processing. The standardized data structure includes pre-established field definitions, data types, and validation rules that enable rapid processing without requiring complex real-time analysis, thus reducing processing time while maintaining data quality
Solution Approach 2:
The patent changes parameters by transforming unstructured or semi-structured medical data into standardized structured data with specific parameters such as fixed field lengths, standardized codes, and predefined relationships. This parameter standardization enables efficient processing across multiple terminals without sacrificing data integrity
2Reliability
If structured information extraction or separate modeling by medical workers is used to generate medical data for AI applications, then basic data quality requirements are met, but processing costs and time consumption increase
Solution Approach 1:
The patent applies universality by creating a standardized data structure that serves multiple functions: it satisfies AI application requirements, enables cross-terminal data interaction, and maintains data quality. This universal structure eliminates the need for separate modeling efforts for different applications, thereby improving processing efficiency while maintaining data quality
Solution Approach 2:
The patent uses preliminary action by pre-establishing standardized data structures with predefined fields, types, and relationships that can be directly applied to various AI applications. This eliminates the need for time-consuming separate modeling by medical workers, as the standardized structure already meets basic quality requirements for multiple uses
3Adaptability or versatility
If medical data from multiple terminals with different requirements are integrated, then comprehensive data coverage is achieved, but data structure complexity increases
Solution Approach 1:
The patent applies homogeneity by transforming diverse data from multiple terminals into a unified standardized structure with consistent field names, data types, and formatting rules. This homogeneous structure enables seamless integration of data from different sources while reducing overall complexity, as all data conforms to the same template regardless of origin
Data Source
AI summary
A method for testing medical data is provided. Each medical datum includes a plurality of information units and a plurality of separators, and the method includes the following steps: a. matching the medical data against a standard library including a plurality of patterns, a matching expression being: [\s\S][number/sequence/relation]&[\b|\B] (S101); and b. determining, based on a matching result of the step a, whether the medical datum is qualified (S102). A standardized standard library is first established, a matching result is obtained by matching the medical datum and the standard library for a non-initial boundary, an initial boundary, an information quantity, information sequences, a semantic relationship quantity, a character boundary, and a non-character boundary, and whether the medical datum meets a requirement is further determined according to the matching result.