Multitier Genetic Classification for Cancer Diagnosis
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Solution Overview
Problem
Current genetic testing systems face challenges in accurately distinguishing between different types of cancer and efficiently analyzing large volumes of genetic data, leading to inaccuracies and impracticality in diagnosing multiple cancer types simultaneously.
Innovation Solution
A multiclass classification model is developed to analyze genetic information, capable of simultaneously identifying multiple cancer types and non-cancerous inputs by processing textual representations of DNA data, reducing computational resources through targeted analysis and sequential model application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If genetic testing systems analyze large volumes of genetic data to identify multiple cancer types, then diagnostic comprehensiveness is improved, but computational resource consumption increases and analysis efficiency deteriorates
Solution Approach 1:
The patent segments the genetic analysis process into multiple tiers: a first tier that performs comprehensive multiclass classification to identify potential cancer types, and a second tier that performs more detailed analysis only on samples flagged as positive in the first tier. This segmentation allows the system to maintain high diagnostic comprehensiveness while improving analysis efficiency by reducing the number of samples undergoing resource-intensive full analysis.
Solution Approach 2:
The system performs partial analysis on all samples through the first tier model, and only performs excessive/detailed action on a subset of samples that require further investigation. This approach ensures that computational resources are concentrated on samples most likely to be positive, thereby improving overall analysis efficiency without compromising diagnostic comprehensiveness.
2Measurement precision
If genetic testing systems perform comprehensive analysis of all genetic data to distinguish between different cancer types, then measurement precision is improved, but computational resource consumption increases
Solution Approach 1:
The patent divides the computational workload into two segments: a lightweight first-tier model that screens all samples for potential cancer presence, and a more computationally intensive second-tier model that performs precise cancer type differentiation only on samples identified as positive in the first tier. This segmentation maintains high measurement precision for cancer type differentiation while significantly reducing overall computational resource consumption.
Solution Approach 2:
The system performs preliminary screening using the first tier model before committing resources to comprehensive cancer type differentiation. This preliminary action filters out negative samples early, ensuring that computational resources are only expended on samples that require precise cancer type differentiation, thereby maintaining measurement precision while reducing overall resource consumption.
3Measurement precision
If genetic testing systems use multiple classification models to identify different cancer types, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements a segmented multi-model approach where the first tier uses a multiclass classification model to identify potential cancer presence across multiple cancer types, and the second tier uses additional classification models only for samples that test positive in the first tier. This segmentation maintains high diagnostic accuracy through multiple models while reducing system complexity by limiting the activation of complex models to only necessary cases.
Solution Approach 2:
The system dynamically adjusts its operational complexity based on the results of the first tier analysis. For negative samples, the system remains simple by not invoking additional models. For positive samples, the system dynamically activates more complex second-tier models to achieve high diagnostic accuracy. This dynamic approach maintains diagnostic accuracy while reducing average system complexity.
Data Source
AI summary
Introduced here is an approach to training a machine learning model to classify a patient amongst multiple cancer types using sets of locations that indicate where mutations typically occur for those multiple cancer types. Upon being applied to genetic information associated with a patient whose health state is unknown, the machine learning model can produce, as input, values that indicate the likelihood of the patient having each of the multiple cancer types. Also introduced here is an approach in which diagnoses are predicted in an improved manner through the application of different models in “tiers” or “stages.” The approach may involve applying a set of multiple models to the genetic information of an individual in order to ascertain the health of the individual, and each of the multiple models can be used to indicate whether the next model in the set should be applied.