Automated Clinical Data Standardization via Machine Learning
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Solution Overview
Problem
Clinical medical data for retrospective research is highly variable in format and content due to differences in data storage formats and human error, requiring significant manual manipulation to generate accurate reports, which can introduce additional errors.
Innovation Solution
Systems and methods for extracting and standardizing clinical data from multiple health data sources using machine-learning algorithms to identify and correct inconsistencies, and generate relevant reports based on user profiles and data analysis needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data manipulation is used to standardize clinical data, then data accuracy can be improved, but time consumption and error introduction increase
Solution Approach 1:
The patent replaces manual data manipulation with automated machine learning algorithms that perform data standardization and quality control. The system uses natural language processing and classification models to automatically extract, standardize, and validate clinical data from multiple sources, eliminating the need for manual intervention while maintaining high accuracy through algorithmic processing
Solution Approach 2:
The system performs self-validation through machine learning quality control checks that automatically identify and correct data inconsistencies. The machine learning models continuously learn from data patterns to improve their own accuracy in standardizing clinical data without requiring external manual correction, enabling the system to serve itself in the data standardization process
2Loss of time
If automated machine learning is used to standardize clinical data, then time consumption is reduced, but data complexity and processing requirements increase
Solution Approach 1:
The patent divides the complex data standardization process into separate modular components: data extraction modules for each data source, machine learning classification models for data standardization, quality control checks for validation, and report generation modules for output. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining automated processing capabilities
Solution Approach 2:
The system introduces standardized data formats and classification models as intermediaries between diverse clinical data sources and the final report generation. These intermediaries translate varied data structures from different sources into a unified standardized format, simplifying the processing requirements and enabling automated handling without direct complex interactions between all system components
3Quantity of substance
If clinical data is collected from multiple data sources, then data comprehensiveness is improved, but data inconsistency and variability increase
Solution Approach 1:
The patent implements universal machine learning classification models that can process and standardize data from multiple different clinical data sources simultaneously. The models are trained to recognize and standardize various data formats and structures from different sources into a consistent unified format, enabling the system to handle diverse data comprehensiveness while maintaining consistency through the universal standardization process
Data Source
AI summary
Methods, apparatus, systems, computing devices, computing entities, and/or the like for generating medical research reports automatically collect data from a plurality of separate health data storage systems, standardize the received data to support at least a requested report type, apply one or more machine-learning quality control check to identify potentially inaccurate data included within the received data, and to generate the requested report based at least in part on the standardized, refined data. Moreover, one or more recommended additional reports supported by the refined data set is identified and recommended to a user based at least in part on user attributes and reports initially requested.