Deep Learning Models with Stratification-based Loss Function on Domain Knowledge-based Time series Data: Hypotension Prediction

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.