Diagnostic Data Mapping for ICD Attribute Equivalency
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Solution Overview
Problem
Conventional diagnostic databases are incapable of transforming data structures from a first type of coding, such as ICD-10, to a second type of coding, such as ICD-9, while maintaining data attribute equivalence, and fail to incorporate user information into the transformation process.
Innovation Solution
A system and method that utilizes a comprehensive data transformation system to map data structures from ICD-10 to ICD-9 coding while maintaining attribute equivalence, incorporating user-level characteristics, and employing iterative gradient descent modification to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional databases are used for data structure transformation, then the transformation process is simple, but the databases are incapable of maintaining data attribute equivalence during transformation
Solution Approach 1:
The patent introduces a specialized transformation system that acts as an intermediary between conventional databases. This system includes a transformation engine with machine learning models that mediate the conversion process, ensuring data attribute equivalence is maintained while handling the complexity of transforming between different coding systems (e.g., ICD-10 to ICD-9).
Solution Approach 2:
The transformation system dynamically adjusts transformation parameters and mapping relationships based on the specific data attributes being transformed. By changing the parameters of the transformation process rather than the data itself, the system maintains attribute equivalence across different coding systems while adapting to various transformation scenarios.
2Measurement precision
If conventional transformation methods are used, then the processing speed is fast, but the transformation accuracy and clinical richness are lost
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing source data structures before transformation to identify and preserve critical attributes. The transformation engine prepares mapping relationships and validation rules in advance, ensuring high transformation accuracy without requiring extensive post-processing time.
Solution Approach 2:
The patent replaces conventional mechanical transformation methods with machine learning-based intelligent transformation. The transformation engine uses trained models to automatically determine accurate mappings between coding systems, substituting rule-based mechanical processes with adaptive intelligent processes that maintain clinical richness while improving accuracy.
3Adaptability or versatility
If simple mapping is applied, then the transformation process is quick, but user information and clinical context are not incorporated
Solution Approach 1:
The transformation system is designed with multi-functionality to handle various transformation scenarios while incorporating user information and clinical context. The engine can adapt to different coding systems, data types, and user requirements through a unified framework, making the system versatile without requiring separate complex systems for each transformation need.
Data Source
AI summary
The invention provides a comprehensive data transformation system, method and computer program product structured for transformation of data structures to maintain data attribute equivalency in diagnostic databases. In some embodiments, the present invention is configured to determine a first source data structure of the first database; transform the first source data structure to a first target data structure of the second database: determining one or more probable target data structures of the second database associated with the first source data structure; constructing a target transformation mapping structure; constructing a plurality of feature weight vectors associated with the target transformation mapping structure; implementing an iterative gradient descent modification of the plurality of feature weight vectors; ending the iterative gradient descent modification of the plurality of feature weight vectors; and initiate, via a user interface, a presentation of the first target data structure on a display device associated with a user device.