BACKGROUND
Intraoperative hypotension (IOH) is associated with an increased risk of postoperative complications. Therefore, in recent years, various models for IOH prediction based on high-dimensional signal data have been developed. Given that the association between the high-dimensionality of data and the overfitting problem, it is very important to establish a strategy to prevent the overfitting problem. However, there has been little discussion of the strategy.
OBJECTIVE
This work aimed to develop an overfitting-resistant deep learning model that uses preoperative patient data along with intraoperative bio-signal information to predict the IOH about 5 minutes prior to its occurrence.
METHODS
Mean arterial blood pressure (2 sec interval) and electronic medical records of 990 patients from open-source database, VitalDB were integrated for this study. The IOH was defined as an MBP < 65 mmHg for >1 min. Our proposed deep learning model accommodates the dropout method for preventing overfitting and the permutation method for reducing the dependence of the American Society of Anesthesiologists (ASA) status on IOH; we permuted the ASA status in the process of model training. The primary outcome was evaluated in terms of the area under the receiver operating characteristic curve (AUROC).
RESULTS
The model with the permutation method showed better performance (AUROC, 95% confidence interval [CI]: 0.842, 0.838-0.845) than that of model without the permutation method (AUROC, 95% CI: 0.830, 0.825-0.835). Furthermore, the model with both the permutation and dropout methods exhibited the best performance (AUROC, 95% CI: 0.862, 0.859-0.861).
CONCLUSIONS
Our work demonstrated the effectiveness of the permutation method in preventing the overfitting problem. Ultimately, the introduction of the permutation of the ASA status and dropout methods into a deep learning model can prevent the overfitting problem and improve the accuracy of IOH prediction.
