Medical Event Classification Using Time-Between-Event Values
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Solution Overview
Problem
Current medical device systems face challenges in detecting adverse changes in event occurrences, particularly when events are rare, as proportion-based and time-based control charts may fail to detect trends promptly or accurately, leading to missed signals and false alarms.
Innovation Solution
A computing system that determines time-between-event (TBE) values and applies classification conditions based on reference data to detect changes in event occurrences, generating control charts with adjustable upper and lower control limits using multiple data points, thereby enhancing responsiveness and accuracy in detecting adverse trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proportion-based control charts are used to monitor event occurrences, then the system can track event frequencies, but it fails to detect trends promptly when events are rare
Solution Approach 1:
The patent transforms the monitoring parameter from event frequency (proportion-based) to time-between-events (TBE), which is more sensitive to trend changes in rare events. This parameter transformation enables earlier detection of adverse changes while maintaining reliability in classification.
2Device complexity
If traditional control charts are used for event monitoring, then the system maintains simplicity in implementation, but it produces false alarms and missed signals
Solution Approach 1:
The system implements feedback mechanisms by continuously updating control limits based on reference data from multiple data points. This dynamic adjustment of control limits reduces false alarms and missed signals while maintaining system simplicity through automated feedback loops.
Solution Approach 2:
The patent establishes classification conditions and control limits in advance using reference data before monitoring begins. This preliminary configuration reduces false alarms by setting accurate baseline expectations, while the system remains simple to operate once configured.
3Measurement precision
If multiple data points are used to establish control limits, then the system improves accuracy in detecting adverse trends, but it increases computational requirements
Solution Approach 1:
The system uses a practical number of reference data points (e.g., 10-50 events) to establish control limits, which provides sufficient accuracy for clinical decision-making without requiring excessive computational resources. This balanced approach achieves adequate measurement precision with manageable computational complexity.
Data Source
AI summary
A computing system including a memory, processing circuitry coupled to the memory, and communications circuitry. The processing circuitry is configured to determine a time-between-event (TBE) value for a first plurality of events detected by a computing device, determine a classification condition based on the determined TBE value and reference data stored in the memory, the reference data comprising information corresponding to prior detected events, based on a determined change in the TBE value, apply the classification condition to apply a to a plurality of detected events, wherein the plurality of detected events comprises the first plurality of events and the second plurality of events, and determine whether one or more of the plurality of detected events satisfies the classification condition. The communications circuitry is configured to communicate to another device via a network that the one or more of the plurality of detected events satisfies the classification condition.