Shock Type Classification via One-Versus-Rest Models
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Solution Overview
Problem
In the medical field, accurately identifying the type of shock (such as cardiogenic, septic, or hypovolemic shock) is crucial for appropriate treatment, but existing methods lack efficiency and accuracy, especially for inexperienced clinicians and in resource-constrained settings.
Innovation Solution
A computer-implemented method using one-versus-rest models and a classification model processes medical data to predict the most probable type of shock by generating numeric values indicating likelihoods, which are then used to produce a predictive indicator, facilitated by machine-learning algorithms and logistic functions, allowing for improved accuracy and resource-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual diagnosis methods are used by clinicians, then treatment decisions can be made, but accuracy and efficiency are reduced especially for inexperienced clinicians
Solution Approach 1:
The patent replaces manual clinical diagnosis (mechanical human judgment process) with an automated machine learning system that processes medical data to predict shock types. The system uses trained models to automatically analyze patient data and generate diagnostic predictions, substituting the manual mechanical process with an automated computational system that improves both accuracy and efficiency.
Solution Approach 2:
The patent introduces an automated prediction system as an intermediary between raw medical data and clinical decision-making. This intermediary system processes and interprets complex medical data, providing structured diagnostic predictions that assist clinicians without replacing their final decision-making authority, thereby improving accuracy while maintaining clinical oversight.
2Adaptability or versatility
If multiple shock types are differentiated using traditional methods, then treatment specificity can be improved, but the complexity of diagnosis increases
Solution Approach 1:
The patent segments the complex task of shock type differentiation into multiple specialized machine learning models, each trained to identify specific shock types (e.g., septic shock model, cardiogenic shock model, hypovolemic shock model). This segmentation allows the system to handle multiple shock types effectively while managing complexity through modular model architecture, where each model focuses on specific diagnostic patterns.
3Ease of operation
If resource-constrained settings are considered, then accessibility can be improved, but diagnostic accuracy may be compromised
Solution Approach 1:
The patent employs machine learning models that can be trained and deployed with varying resource requirements. The system uses parameter optimization techniques to achieve high diagnostic accuracy while adapting to different computational environments, from resource-constrained point-of-care devices to more powerful centralized systems, maintaining performance across diverse deployment scenarios.
Data Source
AI summary
A mechanism or model for shock type classification that is able to differentiate between different types of shock, e.g. among patients with suspected hemodynamic instability. Respective one-versus-rest models are used to generate a numeric value for each of a plurality of shock types, each numeric value indicating a predicted probability that a subject exhibits that particular shock type over other types. A classification process is then performed to select the most likely shock type based on the numeric values.