Pre-symptomatic Agent Exposure Detection Using Physiological Signal Analysis
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Solution Overview
Problem
Current methods for detecting biological or chemical exposure are often ineffective before symptoms appear, relying on costly and logistically burdensome molecular diagnostics and focusing primarily on bacterial infections, with no effective techniques for viral infections or toxic chemical exposure.
Innovation Solution
A system and method using random forest classifiers to analyze physiological data from patients, extracting features like pulmonary, blood pressure, and electrocardiography data to predict exposure to agents such as chemical, biological, or viral pathogens before symptoms occur, by training classifiers on specific post-exposure time intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular diagnostics are used for pre-symptomatic detection, then detection accuracy is improved, but device complexity and operational burden increase significantly
Solution Approach 1:
The patent replaces complex molecular diagnostic systems with a physiological signal-based detection system. Instead of using genomic or transcriptional expression profiles that require specialized equipment and cold chain storage, the invention uses standard physiological monitors to collect and analyze routine physiological data, substituting a simpler mechanical/electronic system for a complex biochemical one.
Solution Approach 2:
The patent creates a computational model that copies the patterns of physiological changes observed in infected individuals and applies it to detect exposure in new subjects. Rather than directly measuring molecular markers, the system uses machine learning classifiers to replicate the detection capability based on physiological signal patterns.
2Reliability
If high-throughput sequencing is used for pre-symptomatic detection, then detection capability is improved, but cost and logistic burden increase
Solution Approach 1:
The patent employs inexpensive, portable physiological monitoring devices that can be deployed widely without requiring expensive infrastructure. These standard monitors are far cheaper than high-throughput sequencing equipment and do not require cold chain storage or specialized facilities, making the system economically viable for broad deployment.
Solution Approach 2:
The patent makes the detection system universally applicable by using standard physiological monitors that are already widely available and multi-functional. The same equipment used for routine health monitoring can be repurposed for exposure detection, eliminating the need for specialized equipment and reducing logistic requirements.
3Ease of operation
If physiological signal analysis is used for early infection detection, then ease of operation is improved, but detection precision for viral infections and chemical exposure deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors physiological signals and compares them against learned patterns of exposure responses. The machine learning classifiers are trained on data from infected and exposed individuals, creating a feedback loop that refines detection accuracy while maintaining operational simplicity through automated analysis.
Solution Approach 2:
The patent analyzes multiple physiological parameters simultaneously rather than relying on a single indicator. By examining changes in heart rate, respiratory rate, temperature, and other vital signs in combination, the system achieves higher detection precision for viral infections and chemical exposure while keeping the operational approach simple and unified.
Data Source
AI summary
Systems and methods are disclosed herein for predicting whether a patient has been exposed to an agent. Physiological data is recorded from the patient during a first time interval, and one or more features are extracted from the physiological data. A plurality of classifiers is identified, wherein each classifier is trained using training data for a respective specific post-exposure time interval. For each classifier and based on a respective subset of the one or more features, a patient state classification that indicates an initial prediction of whether the patient has been exposed to the agent is determined. An indication of a prediction that the patient has been exposed to the agent is provided when a number of patient state classifications indicating a positive initial prediction that the patient has been exposed to the agent exceeds a first threshold.