Neural Network Dialysis Predictor for Hypotension Prevention
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Solution Overview
Problem
Current biofeedback systems during dialysis treatments only provide retrospective reactions to intradialytic hypotensive situations, failing to predict blood pressure drops in advance, which can lead to unnecessary stress and inefficiencies in patient care.
Innovation Solution
A device and method utilizing learning algorithms, such as neural networks, to predict intradialytic parameters like blood pressure by storing patient-specific and machine parameters, allowing for early intervention and automatic adjustments in ultrafiltration rates, temperature, or fluid administration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If retrospective control is used to correct intradialytic hypotensive situations, then interventions can be made based on actual blood pressure measurements, but blood pressure drops cannot be predicted in advance and unnecessary stress occurs on patients
Solution Approach 1:
The system performs preliminary action by predicting future blood pressure trends using neural networks before actual hypotensive episodes occur. The prediction model analyzes current dialysis parameters (ultrafiltration rate, blood flow rate, dialysate temperature) and patient-specific data to forecast blood pressure changes, enabling proactive intervention rather than reactive correction.
Solution Approach 2:
The system implements continuous feedback by monitoring dialysis parameters and patient responses in real-time, feeding this information back to the neural network prediction model. This feedback loop allows the system to refine predictions and adjust dialysis parameters dynamically to prevent blood pressure drops before they occur.
2Stability of the object's composition
If multiple dialysis parameters (temperature, ultrafiltration rate, conductivity) are adjusted to stabilize blood pressure, then hemodynamic stability can be improved, but the complexity of parameter control increases due to reciprocal effects
Solution Approach 1:
The system applies parameter changes by dynamically adjusting dialysis parameters (ultrafiltration rate, dialysate temperature, blood flow rate) based on neural network predictions. The model determines optimal parameter modifications to prevent blood pressure drops, changing parameters proactively rather than reacting to actual changes, thereby simplifying control despite multiple interacting parameters.
3Stability of the object's composition
If blood volume is continuously measured and controlled to follow a predefined progress, then blood volume stability is achieved, but the system still cannot predict blood pressure drops and requires frequent measurements
Solution Approach 1:
The system uses an intermediary approach by introducing a neural network prediction model that acts as a mediator between current dialysis parameters and future blood pressure outcomes. Instead of directly measuring and reacting to blood pressure changes, the model predicts trends based on intermediate parameters (ultrafiltration rate, blood flow rate, dialysate temperature), providing advance warning before actual blood pressure drops occur.
Data Source
AI summary
Devices and methods for the prognosis of intradialytic parameters such as a blood pressure are described, wherein at least one learning algorithm and/or at least one neural network is/are provided. A memory device stores patient-individual intradialytic parameters, laboratory parameters, and/or machine parameters, which can be used in the prognosis of patient-specific parameter progress during a dialysis treatment.