Machine Learning Cancer Risk Stratification Using Molecular Data
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Solution Overview
Problem
Conventional cancer diagnosis and treatment models struggle with accurately stratifying patient cancer risk due to rigid input data requirements, inaccuracy with censored patient data, and challenges in understanding clinically homogeneous patient groups, leading to issues like overtreatment or undertreatment.
Innovation Solution
A computer-implemented method using machine learning models trained with molecular data to determine patient cancer risk, incorporating univariate gene selection, RNA bias correction, and multivariate gene selection, and including a survival model to generate matched treatment strategies based on patient-specific molecular data risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional cancer diagnosis and treatment models are used, then the models are simple and easy to operate, but they require rigidly uniform input data and cannot accurately handle censored patient data
Solution Approach 1:
The patent transforms the input data structure from rigidly uniform requirements to flexible handling of censored data by changing how the model processes survival data. The machine learning model accepts various data types including censored observations and converts them into a unified representation that maintains accuracy without requiring strict data uniformity.
Solution Approach 2:
The patent replaces conventional mechanical classification systems with machine learning models that can automatically learn from diverse data patterns. The ML model substitutes rigid data validation mechanisms with adaptive algorithms that can process censored data, missing values, and varying data formats without requiring manual intervention or strict data formatting.
2Measurement precision
If molecular classification systems are used, then patient risk stratification is improved, but the system cannot adequately address pathogenic and prognostic heterogeneity within subtypes
Solution Approach 1:
The patent segments the heterogeneous cancer population into distinct molecular subtypes using unsupervised learning clustering algorithms. This segmentation allows the model to identify and handle different pathological and prognostic heterogeneities within broader cancer categories, enabling subtype-specific risk assessment and treatment recommendations that account for internal variability.
Solution Approach 2:
The patent implements dynamic risk assessment that adapts to the specific characteristics of each patient's molecular profile. Rather than using static risk categories, the model continuously adjusts risk predictions based on the unique combination of molecular features, allowing it to handle heterogeneity by tailoring assessments to individual patient dynamics.
3Ease of operation
If clinical criteria are used for diagnosis, then the criteria are straightforward, but clinicians cannot adequately diagnose patient risk profiles for clinically homogeneous groups
Solution Approach 1:
The patent introduces molecular data as an intermediary layer between simple clinical criteria and complex patient risk profiles. This intermediary molecular profiling enables the system to maintain the simplicity of clinical diagnostics while adding the precision needed to differentiate risk profiles within homogeneous groups through computational analysis of molecular features.
Solution Approach 2:
The patent adds a molecular dimension to traditional clinical diagnosis, transforming the assessment from two-dimensional (clinical features only) to multi-dimensional (clinical + molecular). This dimensional expansion allows the model to capture subtle risk differences within clinically homogeneous groups by incorporating molecular feature space that was previously inaccessible.
4Reliability
If early stage cancer patients are treated aggressively, then treatment effectiveness is improved, but overtreatment occurs when patients are actually low risk
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor patient outcomes and adjust treatment recommendations accordingly. The model learns from actual patient responses and outcome data, providing feedback that refines risk predictions and prevents overtreatment by adapting recommendations to individual patient trajectories and actual risk realizations.
Solution Approach 2:
The patent dynamically changes treatment recommendation parameters based on individual patient risk profiles rather than using fixed aggressive treatment protocols. The model adjusts treatment intensity parameters continuously, reducing overtreatment by tailoring the degree of aggressiveness to each patient's actual molecular risk characteristics.
Data Source
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AI summary
A implemented method, computing system and computer-readable medium for stratifying patient cancer risk using molecular data includes receiving molecular data; processing the molecular data using a machine learning model; and generating a matched treatment strategy for the patient based upon the patient's molecular data risk. A computer-implemented method, computing system and computer-readable medium for training a machine learning model to stratify patient cancer risk using molecular data includes receiving a patient training dataset, and a reference training dataset; selecting a cohort of patients; selecting a small subset of genes using univariate selection; generating a corrected reference training dataset; selecting a smaller subset of genes using multivariate selection; training a survival model; and (g) selecting a decision threshold to identify a patient population.