Multi-class Machine Learning Model for Microbiome Disease Differentiation
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Solution Overview
Problem
Current risk prediction models using fecal microbiome data can only detect one disease at a time, limiting their ability to simultaneously assess multiple diseases and are confounded by shared microbial signals across different disease phenotypes, leading to potential misclassification.
Innovation Solution
A multi-class machine learning model is developed using metagenomics data from a cohort of individuals with various health conditions to simultaneously predict the risk of multiple diseases, utilizing a training dataset that includes the relative abundance of bacterial species and optimizing sensitivity and specificity through a multi-class machine learning approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a multi-disease risk prediction model is developed, then the ability to simultaneously assess multiple diseases is improved, but the model complexity increases
Solution Approach 1:
The patent segments the multi-disease prediction problem into multiple binary classification models, where each model is trained to distinguish one disease from all others. This segmentation approach allows the system to handle multiple diseases simultaneously while maintaining manageable model complexity through modular architecture.
Solution Approach 2:
The patent creates a universal prediction framework that can assess multiple different diseases using a single integrated system. The multi-class classification model serves multiple functions by simultaneously evaluating risk across various disease types, eliminating the need for separate prediction systems for each disease.
2Ease of operation
If existing risk prediction models are used, then the detection process is simple, but they can only detect one disease at a time
Solution Approach 1:
The patent merges multiple single-disease prediction capabilities into a single multi-class classification model. By combining the detection functions for multiple diseases into one integrated model, the system maintains operational simplicity while significantly expanding disease detection capacity beyond what single-disease models can achieve.
3Measurement precision
If reference data from multiple diseases are used separately, then each disease risk can be determined individually, but the process is time-consuming and inefficient
Solution Approach 1:
The patent implements continuous multi-disease prediction by processing all disease assessments simultaneously in a single model execution. This eliminates the sequential processing time required by separate models, maintaining measurement precision for each disease while dramatically reducing the total prediction time through parallel evaluation of all disease risks.
Data Source
AI summary
This disclosure provides a predictive risk assessment tools to determine personalized risk of multiple diseases in a subject using microbiome. Current risk prediction test using microbiome may only detect one disease or health condition at a time. By determining multiple diseases simultaneously, the disclosed techniques can provide a cost-effective method to support clinical decision making, and hence to help improve disease prevention and management.