JMIR Publications

ABSTRACT

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.