Automated Data Harmonization System for Structured Input
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Solution Overview
Problem
Data from different acquisition devices, such as tomographs, often have varying structures and formats, making uniform automated evaluation and analysis challenging due to differences in data representation and structure, even when dealing with similar data types like leukocyte counts.
Innovation Solution
A system for automated harmonization of structured data includes a harmonization module that converts device-specific data into a globally uniform structure, a preprocessing module for feature reduction, and an automated processing facility using classification or regression models, such as neural networks, to facilitate reliable data processing and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from different acquisition devices are processed directly without harmonization, then device-specific data structures are preserved, but uniform automated evaluation and analysis cannot be performed
Solution Approach 1:
The patent introduces a harmonization module as an intermediary component that receives data in device-specific structures from multiple acquisition devices and transforms them into a unified harmonized structure. This mediator enables automated processing facilities to work with standardized data without directly interfacing with diverse device-specific formats, thus resolving the contradiction between preserving data structure compatibility and enabling automated evaluation.
2Extent of automation
If data harmonization is implemented to enable uniform processing, then automated analysis becomes possible, but processing complexity increases
Solution Approach 1:
The patent divides the data processing system into distinct functional modules: acquisition devices, a harmonization module, and automated processing facilities. This segmentation allows each component to operate independently with well-defined interfaces, reducing overall system complexity while enabling automated processing. The harmonization module is further segmented into specific processing units that handle different aspects of data standardization.
Solution Approach 2:
The harmonization module serves as a universal interface that can handle data from multiple different acquisition devices with varying data structures. By creating a multi-functional harmonization layer that standardizes diverse input formats into a common structure, the system achieves automated processing capability without proportionally increasing complexity in each individual processing path.
3Productivity
If feature reduction is applied in preprocessing, then processing efficiency improves, but information loss may occur
Solution Approach 1:
The preprocessing module applies feature reduction selectively to retain only the most relevant features for automated analysis. Rather than reducing all features uniformly, the system identifies and preserves critical information while removing redundant or less important features. This partial action approach maintains processing efficiency while minimizing information loss by focusing reduction efforts on non-essential features.
Data Source
AI summary
A system for automated harmonization of structured data from acquisition devices, comprisingan input for input data sets in different, acquisition device-specific data structures,a harmonization module embodying a harmonization model configured to transform a respective input data set from the respective system acquisition device-specific structure into at least one harmonized data set in a globally uniform, harmonized data structure of the system,a preprocessing module embodying a preprocessing model configured to transform data from a harmonized data set into data in a model-specific data structure, in particular to perform feature reduction so that a data set with preprocessed data in the model-specific data structure represents fewer features than a corresponding data set in the globally uniform structure, andan automated processing facility configured to automatically process preprocessed data in the model-specific data structure to classify and to generate a loss measure representing a possible processing inaccuracy (loss) and to output it optionally to the harmonization model or the preprocessing model.