Temporal Transduction Classifier for Health Change-Point Detection
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Solution Overview
Problem
Conventional approaches for predicting the health status of monitored entities, such as medical patients or industrial equipment, fail to effectively utilize measurements from entities with unknown health statuses and do not account for chronological constraints, leading to inaccurate assessments and predictions.
Innovation Solution
The implementation of a Max-Margin Temporal Transduction (MMTT) classifier, which incorporates a support vector machine (SVM) with transductive learning and chronological constraints, uses temporal transductive stochastic gradient descent to classify health statuses and detect change-points in time-series data, ensuring that predictions respect the expectation of a healthy state transitioning to unhealthy but not vice versa.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional prediction approaches are used, then the model can be trained with available labeled data, but the model fails to utilize measurements from entities with unknown health statuses and does not account for chronological constraints
Solution Approach 1:
The patent introduces transductive learning as an intermediary mechanism that bridges labeled training data and unlabeled test data. The transductive classifier acts as a mediator that leverages the structure and relationships in unlabeled data to improve prediction accuracy, rather than discarding this information as conventional approaches do.
Solution Approach 2:
The patent applies chronological constraints as a preliminary action before making predictions. By enforcing the physical constraint that health status transitions are irreversible (healthy → unhealthy, but not unhealthy → healthy), the model pre-processes the prediction space to eliminate impossible scenarios, thereby improving reliability.
2Reliability
If conventional approaches are used, then the training process is simpler, but the model cannot enforce chronological constraints that health status changes from healthy to unhealthy and not the reverse
Solution Approach 1:
The patent introduces dynamic constraints into the classification model that adapt to the temporal nature of health status data. The chronological constraint dynamically adjusts the prediction space at each time step, preventing physically impossible transitions while maintaining model flexibility for legitimate health status changes.
Solution Approach 2:
The patent incorporates feedback mechanisms where the model's predictions are continuously checked against chronological constraints. If a prediction violates the irreversible health status transition principle, the model receives feedback and adjusts its output, ensuring chronological consistency is maintained throughout the prediction process.
3Measurement precision
If more labeled data is collected to improve model accuracy, then prediction performance improves, but the cost and time for obtaining labeled data increases
Solution Approach 1:
The transductive learning approach enables the model to serve itself by utilizing the unlabeled test data for improvement. Instead of requiring extensive external labeling efforts, the model leverages the structure and patterns in the unlabeled data itself, combined with the limited labeled data, to achieve high prediction accuracy.
Solution Approach 2:
The patent changes the fundamental parameter of how data is utilized by shifting from a purely inductive approach (training only on labeled data) to a transductive approach (leveraging both labeled and unlabeled data). This parameter change in the learning paradigm allows the model to achieve better performance without increasing the amount of labeled data required.
Data Source
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AI summary
A method of detecting status changes, and a corresponding point-in-time, in monitored entities ME1, ME2, MEn, includes receiving one or more elements of time-series data 233, 236 from one or more sensors 204, 214, the elements of time-series data representing an operational state of the monitored entity, creating a predictive model 238 from the time-series data in a datastore memory 230, applying a transduction classifier 229 to the predictive model, the transduction classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred, and providing an identification of the change in the time-series data and the corresponding point-in-time. In some embodiments the transduction classifier can be a maximum margin classifier having a support vector machine component and a temporal transductive component. A system and a non-transitory computer readable medium are also disclosed.