Machine Learning System for Standardizing Medical Test Data
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Solution Overview
Problem
Medical test results from different providers are often disparate and not communicated effectively, leading to confusion among entities making decisions based on these tests, such as employers, physicians, and government entities.
Innovation Solution
A system that uses machine learning to receive and analyze test data from multiple sources, identify relevant substances or conditions, and generate combined test results with a relevance score, aligning disparate data formats and identifiers to provide standardized and informative reports to authorized users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If test data from multiple different sources are collected and combined, then the completeness and comprehensiveness of medical test results is improved, but the complexity of data integration and standardization increases
Solution Approach 1:
The patent employs an intermediary processing layer that receives test data from multiple disparate sources, standardizes the data formats, and transforms them into a unified structure. This intermediary system acts as a mediator between diverse test data sources and the final consolidated report, resolving format incompatibilities and enabling seamless integration without requiring changes to the original testing systems.
Solution Approach 2:
The system applies parameter changes by transforming various data formats, identifiers, and structures into a standardized parameter set. Different test data sources with varying formats are converted to common parameters, allowing apples-to-apples comparison and integration of results while preserving the original data integrity through reversible transformation processes.
2Measurement precision
If machine learning models are trained to identify and compare substances across different test data sources, then the accuracy of substance identification is improved, but the computational resources and processing time required increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with extensive substance data before actual test data analysis. The models are pre-loaded with knowledge of substance identifiers, formats, and relationships across different testing sources. This preliminary preparation enables rapid, accurate identification during actual processing without requiring intensive real-time computation, thus reducing processing time while maintaining high accuracy.
3Ease of operation
If disparate data formats from different test sources are standardized and aligned, then the usability and interpretability of combined test results is improved, but the complexity of data transformation processes increases
Solution Approach 1:
The patent creates a universal data standard that can accommodate multiple input formats from different test sources. The standardized structure serves as a multi-functional interface that accepts various data types and formats while outputting a unified, interpretable report. This universal approach simplifies usability by providing a consistent output format regardless of the diversity of input sources, while the transformation complexity is encapsulated within the standardization layer.
4Loss of information
If relevance scores are calculated for each substance tested to prioritize information, then the quality and relevance of presented information is improved, but the computational complexity of scoring and ranking increases
Solution Approach 1:
The patent extracts and prioritizes only the most relevant information by calculating relevance scores for each substance and condition. Instead of presenting all test data equally, the system extracts high-value information based on predefined relevance criteria such as clinical significance, frequency of occurrence, and relationship to patient history. This extraction approach improves information quality by focusing on what matters most while reducing the apparent complexity by filtering out less relevant data.
Data Source
AI summary
The present disclosure may include methods, systems, and computer-readable media for optimizing database functionality. For example, one method may include receiving test data from multiple different test data sources for a specified user under test. The method may further include training a machine learning model to: access the test data received from the test data sources for the same user under test, identify which disparate substances or conditions are being tested on the user by each of the different test data sources, and determine a relevance score for each of the identified substances or conditions being tested by the different test data sources. The method may also include generating an illustration of combined test results for the user under test that includes those substances or conditions that meet at least a minimum relevance score. Other systems, methods, and computer-readable media are also described.