Learning Model Predicts Catheter Treatment Time and Fee
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Solution Overview
Problem
Current Percutaneous Coronary Intervention (PCI) treatments face challenges in accurately predicting treatment time and fee, leading to inefficient allocation of catheter treatment rooms and reduced utilization rates, as existing methods only propose catheter shapes without predicting the required time and cost.
Innovation Solution
A program and information processing device that utilize a learning model to predict treatment time and fee by processing patient and treatment information, generating a model that outputs the required time and fee based on training data, allowing for more accurate predictions and improved resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learning model is introduced to predict treatment time and fee, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
A learning model is introduced as an intermediary component between the input data (patient information, treatment information) and the output (predicted treatment time and fee). This mediator processes the complex relationships in the data to provide accurate predictions while keeping the overall system architecture modular and manageable, thus resolving the contradiction between improved prediction accuracy and increased device complexity.
2Productivity
If treatment time prediction is improved, then resource allocation efficiency is improved, but measurement precision requirements increase
Solution Approach 1:
The system incorporates feedback mechanisms where actual treatment time and fee data are continuously collected and used to retrain and refine the learning model. This feedback loop allows the system to improve prediction accuracy over time while adapting to variations in patient conditions and treatment outcomes, thereby enhancing resource allocation efficiency without requiring excessively high initial measurement precision.
Data Source
AI summary
A non-transitory computer-readable program, an information processing method, an information processing device, and a model generation method that can predict a time and a fee required for catheter treatment. According to the program, a computer obtains patient information related to a patient for whom catheter treatment is performed, and treatment information related to the catheter treatment to be performed on the patient. Furthermore, the computer outputs a treatment time and a fee required for the catheter treatment based on the obtained patient information and treatment information.