Risk types of adverse events in medical devices based on deep learning
[Objective]To explore the value of deep learning related techniques in the risk type discrimination of medical device adverse events.[Methods]The study population was selected from April to September 2023,12,350 data in the Medical Device Adverse Event Surveillance System database,each with 25 dimensions of information,including company,registration certificate number,product batch number,and description of adverse events.This study uses fastText technology to convert text features into vector representations,uses k-means clustering method to classify text data with similar themes or semantic content into the same category,and uses BP neural network algorithm to classify the severity of adverse events into mild injury,severe injury,and death.[Results]This method can handle text data well,and the accuracy of the BP neural network model is 92.86％,accuracy is 93.65％,recall is 93.08％,F1 score is 92.31％,AUC is 0.98,with good accuracy and generalization ability.[Conclusion]The research on the risk types of adverse events in medical devices based on deep learning can effectively provide assistance for the monitoring of adverse events in medical devices.
deep learningmedical devicesadverse eventsclassification research
万方数据
维普
