Hyperglycemia Diagnosis via Microbial Feature Selection
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Solution Overview
Problem
Current machine learning models for diagnosing hyperglycemia face challenges due to high noise in unprocessed training data and biases in bacterial metagenome analysis, leading to decreased performance in identifying causative agents of hyperglycemia.
Innovation Solution
A method involving the analysis of a gut-derived substance mixed with a gut environment-like composition to extract microbial data, select microbe-related features from orders such as Oscillospirales, Lachnospirales, Lactobacillales, and Peptostreptococcales-Tissierellales, and train a machine learning model to predict hyperglycemia, reducing noise and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If bacterial metagenome analysis is performed without sample culturing, then the analysis process is simplified, but the accuracy of deriving causative agents of hyperglycemia decreases due to large biases among samples
Solution Approach 1:
The patent applies preliminary action by performing sample culturing before metagenome analysis. This preliminary culturing step enriches the gut microbiota in a controlled environment, reducing biases among samples and improving the accuracy of causative agent identification while maintaining process simplicity
2Loss of time
If unprocessed samples are used as training data, then the data processing workload is reduced, but the machine learning model performance degrades significantly due to large amounts of noise
Solution Approach 1:
The patent applies preliminary action by performing sample culturing and metagenome analysis before machine learning training. This preliminary processing enriches relevant microbial features and reduces noise in the training data, improving model performance while minimizing additional processing time during deployment
3Measurement precision
If microbe-related features are selected from specific orders, then the model focuses on relevant features improving accuracy, but the feature selection process becomes more complex
Solution Approach 1:
The patent applies local quality by selecting microbe-related features from specific taxonomic orders (Oscillospirales, Lachnospirales, Lactobacillales, Peptostreptococcales-Tissierellales) that are locally relevant tohyperglycemia. This targeted feature selection from specific microbial groups improves diagnosis accuracy while keeping the feature selection process manageable through biological guidance
Data Source
AI summary
A method for determining whether hyperglycemia is present by using a machine learning model may include a process of analyzing a mixture of a gut-derived substance collected from a subject and a gut environment-like composition, a process of extracting multiple microbial data based on an analysis result of the mixture, a process of selecting microbe-related features to be used in the machine learning model from the multiple microbial data based on a predetermined feature selection algorithm, a process of training the machine learning model with the microbe-related features, and a process of inputting, to the trained machine learning model, the microbial data collected from the subject to be tested and determining whether hyperglycemia is present. The microbe-related features may include the amount of one or more microbes selected from families included in orders, Oscillospirales, Lachnospirales, Lactobacillales, and Peptostreptococcales-Tissierellales.