ML Classification for Sepsis Detection Using Routine Blood Tests
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Solution Overview
Problem
Current methods for detecting medical diseases or conditions often require specialized tests and are not efficient for rapid and accurate detection.
Innovation Solution
A method using machine learning classification systems to determine the risk of developing or identifying a known disease or condition based on routine patient data, such as results from basic metabolic panels (BMP) and complete blood counts (CBC) with differential, along with vitals and demographics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized tests are used for disease detection, then measurement precision is improved, but device complexity and loss of time increase
Solution Approach 1:
The machine learning model serves multiple diagnostic functions by analyzing results from routine blood tests (BMP and CBC panels) that are already part of standard medical practice. The system processes these universally available tests to detect multiple different conditions including sepsis, COVID-19, and other diseases, eliminating the need for separate specialized tests for each condition.
Solution Approach 2:
The system creates a virtual copy of specialized diagnostic capability by training machine learning models on data from patients with confirmed diagnoses. This digital twin approach allows the routine test results to be reinterpreted through AI algorithms that replicate the diagnostic accuracy of specialized tests without requiring the physical specialized testing equipment.
2Measurement precision
If specialized tests are used for disease detection, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary disease detection analysis by applying machine learning models to routine blood test results immediately when they become available in the electronic medical record. This preliminary action occurs during the standard blood draw process, allowing early identification of high-risk patients before clinical symptoms fully develop, thereby reducing the time to detection without requiring additional specialized testing steps.
3Productivity
If machine learning classification is used with routine patient data, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The machine learning models transform routine blood test parameters into diagnostic predictions by applying complex algorithms that identify non-obvious patterns and relationships among multiple test values. The system changes the interpretation parameters of standard BMP and CBC results, extracting diagnostic information that is not apparent through traditional single-parameter analysis, thereby maintaining high accuracy while improving processing efficiency.
Data Source
AI summary
A method of determining the risk of developing a known disease or condition or of identifying the presence of the known disease or condition in a subject includes obtaining subject data that includes results of blood tests. The blood tests include a basic metabolic panel (BMP) and a complete blood count (CBC) panel. The method further includes classifying the subject data with respect to the risk of the subject having or developing the known disease or condition by using the subject data in a machine learning classification system. The classification system includes a machine learning model previously trained on BMP and CBC data from a positive group of training subjects who received a diagnosis of the disease or condition and from a negative group of training subjects who were not diagnosed to have the disease or condition.