Unified Medical Data Analysis System for Diagnostic Accuracy
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Solution Overview
Problem
Current medical diagnostic approaches fail to synergistically analyze high-dimensional image and non-image medical data effectively, leading to incomplete data sets and inadequate analysis due to disparate data acquisition and analysis across different departments and facilities, which can result in missed diagnoses or delayed detection of disease conditions.
Innovation Solution
A system comprising a database and digital processor that generates features vectors from combined image and non-image medical data, with a features padding component to handle missing data and perform multivariate analysis to determine proposed diagnoses, ensuring comprehensive data analysis and outputting human-perceptible results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple imaging and nonimaging diagnostics are used to provide complementary patient information, then diagnostic accuracy and completeness are improved, but data integration and analysis complexity increase
Solution Approach 1:
The patent combines multiple disparate medical data sources (imaging data, non-imaging data, electronic health records, genomic data, proteomic data, metabolomic data) into a unified data structure with standardized schemas. This merging approach enables comprehensive multivariate analysis while managing complexity through consistent data organization and integration protocols.
Solution Approach 2:
The system creates a universal data structure and analysis platform that can handle multiple types of medical data (imaging, non-imaging, genomic, proteomic, metabolomic) through a single integrated framework. This multi-functional approach allows the same system to process diverse data types using common methodologies, reducing overall system complexity.
2Quantity of substance
If data are acquired and analyzed across different departments and facilities, then comprehensive data collection is improved, but data consistency and completeness deteriorate
Solution Approach 1:
The patent segments the integrated data system into modular components with standardized interfaces. Each data source (imaging, non-imaging, genomic, etc.) is handled as a separate module with defined data schemas and validation rules, allowing consistent integration across multiple facilities while maintaining data quality standards.
Solution Approach 2:
The system applies parameter standardization and normalization transformations to data from different sources. By converting diverse data formats and measurement scales into unified parameters, the system maintains consistency across departments and facilities while preserving the completeness of collected data.
3Productivity
If high-dimensional data are analyzed using traditional univariate methods, then computational simplicity is maintained, but diagnostic sensitivity and specificity decrease
Solution Approach 1:
The patent replaces traditional univariate statistical methods with multivariate analysis approaches that can simultaneously evaluate multiple data dimensions. This substitution enables the system to process high-dimensional medical data more efficiently while improving diagnostic sensitivity through methods like support vector machines, random forests, and other machine learning algorithms.
Data Source
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AI summary
An apparatus comprises: a database (30) storing medical data including image medical data and non-image medical data for a plurality of patients; a digital processor (40) configured to (i) generate a features vector (56) comprising features indicative of a patient derived from patient medical data stored in the database including both patient image medical data and patient non-image medical data and (ii) perform multivariate analysis (64) on a features vector generated for a patient of interest to determine a proposed diagnosis for the patient of interest; and a user interface (42) configured to output a human perceptible representation of the proposed diagnosis for the patient of interest.