Machine Learning Transition Prediction Using Unstructured Health Records
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Solution Overview
Problem
Conventional data analytics systems struggle to accurately predict the progression of conditions due to the lack of suitable training data sources, particularly relying heavily on traditional blood and urine testing, and are unable to identify factors influencing condition progression effectively.
Innovation Solution
Utilize a machine learning model trained with structured and unstructured data sources, including electronic health records, to generate data indicative of condition transitions, reducing reliance on traditional testing methodologies and improving prediction accuracy and factor identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analytics systems rely on traditional blood and urine testing, then measurement methods are established, but prediction accuracy and factor identification capability deteriorate
Solution Approach 1:
The patent transforms the input parameters from traditional blood and urine test results to unstructured electronic health record data including clinical notes, discharge summaries, and provider observations. This parameter transformation enables the machine learning model to identify diverse factors influencing condition progression that were previously inaccessible through conventional testing methodologies
Solution Approach 2:
The patent replaces the mechanical testing system (blood draws, urine collection, laboratory analysis) with an information processing system that extracts and analyzes unstructured data from electronic health records. This substitution eliminates the limitations of traditional testing while enabling comprehensive factor identification through natural language processing and machine learning
2Measurement precision
If machine learning models use traditional training data sources, then data availability is maintained, but prediction accuracy deteriorates
Solution Approach 1:
The patent adds a new dimension to training data by incorporating unstructured text data from electronic health records alongside traditional structured data. This dimensional expansion transforms the training dataset from limited laboratory values to a multidimensional corpus including clinical narratives, patient history, and provider observations, dramatically improving prediction accuracy
Solution Approach 2:
The patent merges structured data (laboratory results, demographics) with unstructured data (clinical notes, discharge summaries) into a unified training dataset for the machine learning model. This combination creates a comprehensive training corpus that captures both quantitative measurements and qualitative clinical insights, enabling more accurate transition predictions
3Adaptability or versatility
If conventional analytics methods are used, then system simplicity is maintained, but ability to analyze unstructured data deteriorates
Solution Approach 1:
The patent introduces natural language processing and text extraction modules as intermediaries between the electronic health record system and the machine learning model. These intermediary components transform unstructured clinical text into structured features that the predictive model can process, enabling unstructured data analysis without overwhelming system complexity
Solution Approach 2:
The patent segments the data processing system into distinct functional modules: data extraction from electronic health records, text preprocessing and feature engineering, machine learning model training, and prediction output. This segmentation allows each component to be optimized independently while maintaining overall system manageability and enabling sophisticated unstructured data analysis
Data Source
AI summary
A method includes receiving, by one or more processors, a dataset including transition data and factor data. The method includes generating a feature for a machine learning model based on the transition data, generating, via input of at least the feature into the machine learning model, one or more data objects indicative of a transition prediction for a transition from the first stage to the second stage, the machine learning model having been trained: with data sources including training factor data having information other than a chemical constituent of blood, and to output information associated with a transition prediction. The method further includes initiating performance of one or more remedial or analytical actions in response to generating the one or more data objects indicative of the transition prediction.