Urine Output Prediction Interface Using Patient Trend Data
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Solution Overview
Problem
Existing physical condition estimating systems do not consider outputting a predicted urine output based on multiple urine outputs measured from the same patient within a predetermined period.
Innovation Solution
A medical device and method that utilize a machine learning model trained with urine output data from various patients to predict future urine output levels, incorporating vital signs, medication information, and body fluid balance, and provide alerts and intervention suggestions based on urine discharge types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained with urine output data from various patients to predict future urine output, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The machine learning model is trained in advance with urine output data from multiple patients to establish prediction capabilities before actual use. The model learns patterns and relationships between urine output measurements and future outcomes, enabling accurate predictions without requiring complex real-time calculations during patient monitoring.
Solution Approach 2:
The system uses data from various patients to create a predictive model that can be applied to individual patient cases. By learning from aggregated patient data, the system captures general physiological patterns that can be replicated across different patients, improving prediction accuracy without requiring patient-specific complex modeling.
2Reliability
If multiple urine outputs measured from the same patient within a predetermined period are utilized for prediction, then prediction reliability is improved, but information processing complexity increases
Solution Approach 1:
The system continuously collects and utilizes multiple urine output measurements from the same patient over a predetermined period. By maintaining a continuous stream of data collection and processing, the system builds a comprehensive picture of patient physiology, improving prediction reliability through consistent monitoring rather than isolated measurements.
Solution Approach 2:
The system processes multiple urine output measurements and uses the results to refine predictions. The feedback loop allows the system to learn from actual patient data patterns, adjusting predictions based on observed trends and improving reliability over time while managing processing complexity through systematic analysis.
Data Source
AI summary
A medical device for determining a future urine output for a patient includes a display and a processor configured to perform the steps of: acquiring first information regarding urine outputs measured from a patient at a plurality of time points, executing a call to a machine learning model with the first information to determine a predicted urine output level that is expected after a predetermined time interval from the last time point, the machine learning model having been trained with: a plurality of urine outputs measured from various patients at a plurality of time points, and target urine outputs for the various patients, generating a screen showing: a graph showing the measured urine outputs over time, and the predicted urine output level, and controlling the display to display the generated screen.