This paper aims to automatically augment numerical tabular data by using the variational autoencoder model. For this, we try to solve the problem of class imbalance in numerical data and to improve the performance of the classification model by augmenting the training data. In this paper, we propose a new augmentation technique called ‘D-VAE’ which performs data augmentation through variational autoencoder with discretization for numerical columuns; D-VAE artificially increases the number of records and the number of columns for a given tabular data. The main features of the proposed technique are to kperform discretization and feature selection in the preprocessing process. For the discretization process, we use k-means algorithm, through which records within a given table are grouped, and then converted into one-hot vectors according to the clustering results. In addition, for memory efficiency, we reduced the number of parameters of the VAE model by using a relatively small number of features through feature selection called REFCV. To evaluate the performance of the proposed technique, we conducted various experiments by numerical data augmentation ratio using four open datasets.

Intraoperative hypotension (IOH) negatively affects the prognosis after surgery. Therefore, in recent years, various studies for IOH prediction based on bio-signal data have been carried out. This paper aims to develop an overfitting-resistant prediction model to forecast 5-minute prior to IOH by domain knowledge-based loss stratification and permutation method. In general, when developing machine learning-based prediction models, we experience the overfitting problem. In our paper, we tried to overcome the overfitting problem by using biomedical domain knowledge. As an example of the domain knowledge, we adopt American Society of Anesthesiology (ASA) status; ASA at higher levels indicates the higher possibility of IOH. To obtain the ASA status for developing the IOH prediction model, we used the electronic medical records from a public database VitalDB. Our proposed deep learning model accommodates the loss stratification and the ASA status permutation to consider the domain knowledge. We have found that the model has shown superior IOH prediction performance according to ASA status; this is particularly because it reduces the dependence of ASA status in the learning process.