Machine Learning Infection Classification System
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Solution Overview
Problem
Current methods for differentiating between primary and latent cytomegalovirus (CMV) infections are imperfect, often yielding ambiguous results and failing to accurately determine the timing of primary infections, which is clinically useful.
Innovation Solution
The use of machine learning techniques, including convolutional neural networks, to analyze immunological data and classify subjects as having latent or primary CMV infections, and to determine the time since CMV exposure or infection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used to analyze immunological data, then measurement precision of infection classification is improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as an intermediary between raw immunological data and clinical diagnosis. The model processes complex antibody profile data and translates it into actionable infection status classifications, resolving the contradiction by using a computational mediator to achieve high precision without requiring direct complex device intervention in the diagnostic workflow
Solution Approach 2:
The patent replaces traditional mechanical/chemical diagnostic methods with computational analysis. Instead of using complex laboratory equipment and manual interpretation of immunological data, the system uses machine learning algorithms to automatically classify infection status, substituting physical diagnostic mechanisms with information-processing mechanisms that achieve higher precision with simpler physical infrastructure
2Measurement precision
If machine learning techniques are used to determine time since infection, then measurement precision of infection timing is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent transforms the difficult-to-measure parameter of 'time since infection' into measurable changes in antibody profile parameters. By monitoring how antibody levels and patterns evolve over time, the machine learning model can infer infection timing from these parameter changes, converting an elusive temporal measurement into detectable immunological parameter variations
Solution Approach 2:
The patent performs preliminary analysis of extensive immunological data parameters before attempting to determine infection timing. The machine learning model pre-processes and identifies relevant patterns in antibody profiles, making the subsequent timing determination more feasible by reducing the complexity of the detection task through prior data preparation and pattern recognition
Data Source
AI summary
Systems and methods for identifying subjects as having latent or primary herpesvirus infections and/or determining the time since the subject was exposed to or infected with a herpesvirus using machine learning algorithms are disclosed. An example method includes detecting in a bodily fluid sample from a subject a set of anti-virus antibody features and generating an input vector that includes data indicative of the anti-virus antibody features of the subject. The method also includes applying the input vector to a trained machine learning algorithm that is configured to generate an assigned classification to the subject. The assigned classification is one of a plurality of potential classifications of the machine learning algorithm. The method also includes determining whether the subject is a suitable candidate for therapeutic intervention based on the assigned classification and, responsive to determining that the subject is suitable, providing the therapeutic intervention to the subject to improve health outcomes.