Data Generation With Filtered <italic>&#x03B2;</italic>-VAE for the Preoperative Prediction of Adverse Events

Abstract

Adverse events after surgery not only affect the patient’s recovery but also increase the burden on doctors and patients due to prolonged hospitalization. Predicting adverse events from patient data before surgery with a machine learning method is highly expected. It is difficult to collect a large amount of patient data since the number of surgeries in a year is limited and predict the occurrence of adverse events accurately since patient data are imbalanced data. To improve the accuracy of adverse event prediction, this paper proposes data generation with Filtered $\beta $ -VAE for the preoperative prediction of adverse events. Filtered $\beta $ -VAE has filters by the reconstruction error and by a machine learning method. After $\beta $ -VAE generates minority class data, the two layers of filtering are used to remove low-quality minority class data that have little contribution to the adverse event prediction. In the evaluations, patient data obtained from Tokyo Dental University Ichikawa General Hospital were used. The proposed method can predict adverse events with a recall of 0.848, which is 5.6% more accurate than existing methods. The effects of filtering in Filtered $\beta $ -VAE are visualized, and the reasons for the improvement in prediction accuracy are clarified. Furthermore, this paper shows that $\beta $ -VAE can generate arbitrary patient data even in table data, corresponding to the distribution of the original patient data.

Keywords

• Machine learning
• data generation
• medical
• adverse events