Saliva Microbiota Prediction for Stratified Neurodegenerative Disease Risk
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods fail to stratify and differentiate various neurodegenerative diseases using biomarkers, and there is a need for early diagnosis and risk determination of these diseases.
Innovation Solution
A method and apparatus using a prediction model generated by machine learning, which utilizes representation abundances of multiple types of bacteria from salivary microbiota analysis to estimate risks of neurodegenerative diseases through stratification, differentiating between healthy subjects and multiple diseases such as Alzheimer's, Parkinson's, and dementia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning prediction model using multiple bacterial types is used, then measurement precision of disease risk determination is improved, but device complexity increases
Solution Approach 1:
The patent extracts and focuses on a specific subset of bacterial types that show significant differences between disease groups, rather than analyzing all possible bacteria. This selective extraction of key bacterial markers simplifies the prediction model while maintaining high diagnostic accuracy for differentiating neurodegenerative diseases
Solution Approach 2:
The patent changes the parameter of bacterial selection by focusing on bacteria with high representation abundances and significant differential expression between disease groups. This parameter optimization allows the model to achieve high precision with a manageable number of bacterial markers
2Measurement precision
If stratification to differentiate multiple diseases is implemented, then measurement precision is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments the complex task of neurodegenerative disease diagnosis into distinct disease groups (Alzheimer's, Parkinson's, dementia with Lewy bodies, and healthy controls) and identifies specific bacterial markers for each group. This segmentation enables targeted analysis and simplifies the detection process for each disease category
3Measurement precision
If multiple bacterial types with high representation abundances are analyzed, then measurement precision is improved, but loss of time in analysis increases
Solution Approach 1:
The patent extracts only the most relevant bacterial types that show significant differences between disease groups, eliminating the need to analyze all bacterial species. This extraction of key markers dramatically reduces analysis time while preserving diagnostic accuracy
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Provided is a method and apparatus for risk determination which uses an easily obtainable saliva sample to estimate risks of neurodegenerative diseases through stratification which differentiates multiple diseases. The invention provides a method of risk determination which estimates risks of neurodegenerative diseases through stratification which differentiates multiple diseases, the method executed by a computer processor, comprising a step of acquiring representation abundances of multiple types of bacteria from an analysis of salivary microbiota of a test subject; and a step of inputting the acquired representation abundances into a prediction model to estimate risks of neurodegenerative diseases occurring in the test subject through stratification which differentiates multiple diseases, wherein the prediction model has been generated by machine learning which entails obtaining an algorithm using, as explanatory variables, representation abundances of multiple types of bacteria acquired from an analysis of salivary microbiota of healthy subjects and patients of multiple diseases belonging to the neurodegenerative diseases, and the disease states of the healthy subjects and the patients stratified across the multiple diseases, output as target variables, as training data, wherein said multiple types of bacteria include bacteria types having high representation abundances and/or bacteria types showing significant differences in the representation abundances between patients of different diseases in the analysis of the salivary microbiota of the patients stratified.