Crisis Message Classifier with Interpretability Layer
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Solution Overview
Problem
Current crisis message classification technologies are limited by being trained on specific datasets, using black box models, lacking integration into clinical workflows, and not providing real-time deployment and feedback, making them ineffective for broad patient crises and chat messages.
Innovation Solution
A method for crisis message classification using a trained natural language processing model that integrates with clinical workflows, provides real-time deployment, and facilitates feedback, allowing for the classification of diverse crisis messages through a user interface accessible via messaging applications, with re-training based on operator and patient input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a knowledge-graph-based model is used to detect crisis messages, then detection capability is improved, but the model becomes a black box and loses interpretability
Solution Approach 1:
The patent introduces an intermediary layer between the neural network model and the user interface that provides explanations and interpretations of model predictions. This mediator translates the black box outputs into understandable clinical insights while preserving the high detection capability of the neural network.
Solution Approach 2:
The system implements feedback loops where crisis operators can provide corrections and validations of model predictions. This feedback is used to continuously improve model interpretability and accuracy, allowing the system to learn from operator decisions while maintaining detection precision.
2Measurement precision
If a model is trained on a specific dataset (e.g., Cantonese dataset), then detection accuracy for that dataset is improved, but the model lacks adaptability to other datasets and languages
Solution Approach 1:
The system implements dynamic model adaptation where the crisis message classifier can be retrained and updated with new datasets over time. The model transitions from a static, dataset-specific classifier to a dynamic system that evolves and adapts to different languages and crisis types through continuous learning from operator feedback and new data.
Solution Approach 2:
The patent designs a universal crisis detection system that can handle multiple languages and crisis types through a single adaptable model architecture. The system uses language-agnostic feature extraction and can be applied to diverse datasets including but not limited to Cantonese, making it versatile across different populations and crisis scenarios.
3Loss of time
If real-time deployment is implemented, then response time to patient crises is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the crisis detection process into distinct modular components: message intake, classification, alert generation, and feedback collection. Each module can be independently optimized and deployed, reducing overall system complexity while enabling real-time processing through parallel operation of segments.
4Adaptability or versatility
If the system monitors all chat messages, then crisis detection coverage is improved, but processing load and computational resources increase
Solution Approach 1:
The system implements partial monitoring where not all messages require full classification processing. Instead, the model focuses computational resources on messages that exhibit crisis-indicative patterns, processing only those messages that meet certain thresholds or show signs of potential crises, thereby reducing overall computational load while maintaining detection coverage.
Data Source
AI summary
Techniques for crisis message classification and/or training a crisis message classifier are disclosed. In one particular embodiment, the techniques may be realized as a method for crisis message classification comprising the steps of receiving patient data from a patient, classifying the patient data into an output with a crisis message classifier based on a cost ratio, responsive to determining that the output indicates a crisis, sending an alert to a crisis responder through a user interface, and receiving an input from the crisis responder through the user interface.