Cancer Classifier Models Using Longitudinal Biomarker Data
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Solution Overview
Problem
Current cancer screening methods suffer from high false positives and negatives, lack of standardization, and insufficient integration into healthcare practices, making it difficult for primary care providers to effectively triage patients for early cancer detection.
Innovation Solution
A machine learning system generates classifier models using longitudinal data from biomarkers and clinical parameters to predict an increased risk of cancer, iteratively improving its performance through feedback from diagnostic tests, and classifying patients into risk categories or organ system malignancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cancer screening tests are used, then cancer detection is performed, but false positives and false negatives increase
Solution Approach 1:
The patent combines multiple biomarker measurements (proteins, metabolites, lipids) into a composite risk score, integrating diverse biological information to improve diagnostic accuracy and reduce false positives compared to single-marker tests
Solution Approach 2:
The system transforms raw biomarker concentrations into standardized risk scores through statistical modeling, changing the parameter representation from absolute concentrations to relative risk probabilities that account for population distributions and clinical thresholds
2Measurement precision
If large prospective studies are conducted to validate screening tools, then data accuracy improves, but costs increase significantly
Solution Approach 1:
The patent performs preliminary validation using retrospective cohort data and existing biobanks before deployment, allowing the screening tool to be pre-tested and optimized using historical data, thereby reducing the need for expensive new prospective studies
Solution Approach 2:
The system incorporates iterative refinement where initial screening results feed back into model optimization, allowing continuous improvement of accuracy using real-world performance data without requiring complete redeployment of large validation studies
3Ease of operation
If binary decision output is provided for cancer screening, then ease of interpretation improves, but diagnostic accuracy decreases
Solution Approach 1:
The patent segments the diagnostic output into multiple risk categories (low, intermediate, high risk) rather than a single binary decision, allowing graduated interpretation that preserves diagnostic nuance while maintaining clinical usability through clear categorization
Solution Approach 2:
The system adds a probabilistic dimension to the output by providing both risk category classification and numerical probability estimates, transforming a one-dimensional binary decision into a two-dimensional output that includes both classification and confidence level
4Measurement precision
If comprehensive biomarker panels are analyzed, then diagnostic accuracy improves, but system complexity increases
Solution Approach 1:
The patent extracts and focuses on a specific panel of clinically relevant biomarkers rather than analyzing all possible molecular markers, selecting only those with demonstrated prognostic value to maintain accuracy while reducing computational complexity
Data Source
AI summary
Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.