Machine Learning Biomarker Prediction from Claims Data
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Solution Overview
Problem
Current healthcare systems face challenges in accurately assessing individual health statuses due to limited availability of biomarker data, which hinders accurate medical assessments and disease severity evaluations, especially when lab values are unavailable or incomplete.
Innovation Solution
A machine learning-based system that predicts biomarker values using claims-based and prescription-based electronic data, enabling the identification of health risks and treatment effectiveness without relying on measured biomarker values, and supports the identification of care gaps and interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lab tests and measured biomarker values are used for health status assessment, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical/lab-based biomarker measurement system with an information-processing system that uses machine learning models to predict biomarker values from claims and prescription data. This substitution eliminates the need for physical lab tests while maintaining health status assessment capability.
Solution Approach 2:
The system creates a virtual copy of biomarker measurement functionality by training machine learning models on historical data to predict biomarker values. This digital copy replicates the information-gathering function without requiring actual laboratory infrastructure.
2Device complexity
If claims-based and prescription-based data are used to predict biomarkers, then device complexity is reduced, but measurement precision may worsen
Solution Approach 1:
The system changes the input parameters from physical biomarker measurements to digital health data (claims and prescription information). By transforming the data type from laboratory measurements to recorded health information, the system maintains predictive accuracy while reducing operational complexity.
Solution Approach 2:
The machine learning model acts as an intermediary that translates claims and prescription data into predicted biomarker values. This intermediary layer bridges the gap between simple digital data and the need for accurate health assessments, maintaining precision through sophisticated algorithms.
3Productivity
If machine learning models predict biomarker values, then productivity is improved by reducing the need for lab tests, but loss of information occurs when measured values are unavailable
Solution Approach 1:
The system performs preliminary action by training machine learning models on historical data where actual biomarker measurements are available. This training phase captures the essential information patterns, allowing the model to predict biomarker values for individuals without requiring actual measurements, thus preventing information loss while maintaining assessment accuracy.
Data Source
AI summary
A system, method, and apparatus are provided that include: providing a dataset to a machine learning model, where the dataset includes claims-based electronic data; receiving an output from the machine learning model in response to the machine learning model processing at least a portion of the dataset, where the output includes a predicted value of a biomarker; processing the portion of the dataset for identifying information associated with an individual in response to determining the predicted value of the biomarker satisfies one or more criteria; and transmitting, via a communication network to one or more communication devices, an electronic communication including information associated with the predicted value of the biomarker.