Medication Risk Evaluation Model Noise Exclusion
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Solution Overview
Problem
Existing medication risk evaluation methods face challenges in accurately assessing the interactions between multiple medications, which can lead to adverse effects when taken concurrently, due to noise and uncertainty in the data used to establish risk evaluation models.
Innovation Solution
A medication risk evaluation method and device that utilize screened medication route information to establish a risk evaluation model by excluding noise-excluded medication route information, using a processor and storage circuit to obtain and analyze medication route data, determine overlap ratios, and establish a risk evaluation model based on relevant data, thereby improving model reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all medication route information is used to establish the risk evaluation model, then the model has higher data coverage, but the reliability decreases due to noise and uncertainty
Solution Approach 1:
The patent extracts and removes medication route information that contains noise or uncertainty from the dataset used to establish the risk evaluation model. By identifying and excluding problematic data points, the model is trained on cleaner, more reliable information, thereby improving overall model reliability while retaining sufficient data coverage for accurate risk assessment.
2Reliability
If noise exclusion criteria are applied to filter medication route information, then the model reliability improves, but the evaluation process complexity increases
Solution Approach 1:
The patent applies noise exclusion criteria in advance during the data preparation phase, before the model establishment process. By pre-filtering medication route information to remove noisy data points, the system simplifies the subsequent model training and evaluation processes, reducing overall complexity while ensuring high model reliability.
3Measurement precision
If overlapping medication information is analyzed to identify noise, then the accuracy of noise detection improves, but the computational time increases
Solution Approach 1:
The patent creates overlapping medication information by comparing medication routes across different patients or time periods. This copying and comparison approach enables accurate identification of noise patterns and anomalies in medication data, allowing the system to detect unreliable information with high precision while managing computational requirements through efficient comparison algorithms.
Data Source
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AI summary
A medication risk evaluation method and a medication risk evaluation device (10) are provided. The medication risk evaluation method includes: obtaining first medication route information related to a first medicine combination in a medication database (101); obtaining second medication route information related to a second medicine combination in the medication database (101); obtaining overlapping medication information between the first medication route information and the second medication route information; determining whether the overlapping medication information meets a noise exclusion condition; and if the overlapping medication information meets the noise exclusion condition, establishing a risk evaluation model (102) based on other medication route information in the medication database (101) excluding the first medication route information, where the risk evaluation model (102) is adapted to evaluate a risk of using at least one medicine in the medication database (101).