Medical Fault Prediction Feedback for Dialysis Device Maintenance
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Solution Overview
Problem
Medical devices with multiple sensors face challenges in predicting faults, leading to reduced patient care capacity and potential treatment delays due to complex fault diagnosis requiring expert technicians.
Innovation Solution
A computer-implemented method for predicting medical device defects, involving a model that predicts future faults based on current operating conditions, determines complexity, transmits predictions to appropriate receivers, and adjusts parameters based on actual conditions through a feedback loop, using sub-models for probability and cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a model predicts all fault conditions and transmits them to all receivers, then comprehensive fault coverage is achieved, but communication overhead and processing complexity increase
Solution Approach 1:
The patent applies local quality by transmitting fault predictions selectively to specific receivers based on the type and severity of predicted fault. Different receivers (e.g., users, service technicians, manufacturers) receive different subsets of predictions tailored to their roles and capabilities, rather than all predictions being broadcast to all receivers.
Solution Approach 2:
The patent segments the fault prediction system by dividing receivers into different groups based on their capabilities and roles. The model determines which receivers should receive which predictions, creating a segmented communication structure that reduces overall system complexity while maintaining comprehensive fault coverage.
2Measurement precision
If the model is trained with all available data and all sub-models are updated, then prediction accuracy is maximized, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively updating only certain sub-models based on feedback from actual fault conditions. Instead of retraining the entire model with all data each time, the system identifies which specific sub-models need adjustment and updates only those, reducing training time while maintaining prediction accuracy.
Solution Approach 2:
The system implements a feedback mechanism where actual fault conditions are compared with predicted faults, and this feedback is used to selectively adjust parameters of specific sub-models. This feedback-driven selective updating improves accuracy over time without requiring complete model retraining.
3Measurement precision
If expert technicians are deployed for complex fault diagnosis, then accurate cause identification is achieved, but operational costs and response time increase
Solution Approach 1:
The patent introduces an intermediary layer (the predictive model and complexity determination system) that processes fault predictions before they reach human technicians. The system automatically determines prediction complexity and routes only appropriate cases to human experts, acting as a mediator that filters and prepares information for human decision-making.
Solution Approach 2:
The system enables self-service by automatically monitoring device conditions, predicting potential faults, and transmitting relevant information to appropriate receivers without requiring constant human intervention. Routine predictions are handled automatically, freeing expert technicians to focus only on complex cases that require human judgment.
Data Source
AI summary
A computer-implemented method for training a model for predicting medical device defects includes: predicting, by the model, a future fault condition of a medical device based on a current operating condition of the medical device; determining a complexity of the predicted fault condition; transmitting the predicted fault condition to a receiver based at least in part on the complexity of the predicted fault condition; receiving information about an actual operating condition of the medical device; and adjusting a set of parameters of the model based at least in part on the information about the actual operating condition and the predicted fault condition of the medical device. The medical device may be, for example, a hemodialysis (HD) device or a peritoneal dialysis (PD) device.