Machine-Learning Bacteremia Assessment from Blood Cell Data
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Solution Overview
Problem
Current clinical methods for diagnosing bacteremia are slow and prone to false positives, leading to unnecessary laboratory tests and increased medical expenses.
Innovation Solution
A method and system utilizing a blood analysis database and machine learning algorithms to train a bacteremia assessing classifier, analyzing cell population and differential counting data for rapid and accurate bacteremia assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blood culture method is used to diagnose bacteremia, then diagnostic accuracy can be improved, but diagnosis time is significantly increased (16-25 hours)
Solution Approach 1:
The patent performs preliminary analysis of blood samples using flow cytometry to identify bacterial cells and generate cell population data before the traditional blood culture process completes. This preliminary action provides early diagnostic information that can guide clinical decisions while the slower culture method continues, effectively reducing the time loss without sacrificing diagnostic accuracy.
Solution Approach 2:
The patent introduces flow cytometry analysis as an intermediary method between direct rapid testing and traditional culture. This intermediary approach uses cell population characteristics to infer bacteremia presence, providing a middle-ground solution that balances speed and accuracy better than either extreme alone.
2Reliability
If blood culture method is used, then bacteremia can be detected, but false positive results increase due to contamination
Solution Approach 1:
The patent analyzes specific local characteristics of blood cells using flow cytometry, examining cell size, granularity, and other localized properties to identify bacterial cells. This local quality analysis provides multiple independent measurement dimensions that can cross-validate results, reducing false positives from contamination while maintaining detection capability.
Solution Approach 2:
The patent performs partial analysis of blood samples by examining only the cellular components relevant to bacteremia detection through flow cytometry, rather than waiting for complete culture growth. This partial action provides sufficient information for accurate diagnosis without the full time and contamination risk of traditional culture.
3Loss of information
If traditional blood culture procedure is followed, then comprehensive bacterial analysis is achieved, but additional laboratory tests and treatments are required
Solution Approach 1:
The patent makes the flow cytometry analysis system multi-functional by using it both for rapid bacteremia screening and for characterizing cell population details. This universal approach consolidates multiple diagnostic functions into a single test, reducing the need for separate laboratory procedures and improving medical resource efficiency while maintaining information completeness.
Data Source
AI summary
A method for assessing bacteremia includes the following steps. A blood analysis database is provided. A model establishing step is performed, wherein a plurality of reference cell population data, a plurality of reference complete blood counting data and a plurality of reference white blood cell differential counting data of the blood analysis database are trained to achieve a convergence by a machine learning algorithm model so as to obtain a bacteremia assessing classifier. A blood analysis data of a subject is provided, wherein the blood analysis data includes a cell population data, a complete blood counting data and a white blood cell differential counting data. An assessing step is performed, wherein the blood analysis data is analyzed by the bacteremia assessing classifier so as to obtain an assessing result of bacteremia of the subject.