Quantitative Analysis of Anesthesia Recovery Time by Machine Learning Prediction Models

Abstract

It is significant for anesthesiologists to have a precise grasp of the recovery time of the patient after anesthesia. Accurate prediction of anesthesia recovery time can support anesthesiologist decision-making during surgery to help reduce the risk of surgery in patients. However, effective models are not proposed to solve this problem for anesthesiologists. In this paper, we seek to find effective forecasting methods. First, we collect 1824 patient anesthesia data from the eye center and then performed data preprocessing. We extracted 85 variables to predict recovery time from anesthesia. Second, we extract anesthesia information between variables for prediction using machine learning methods, including Bayesian ridge, lightGBM, random forest, support vector regression, and extreme gradient boosting. We also design simple deep learning models as prediction models, including linear residual neural networks and jumping knowledge linear neural networks. Lastly, we perform a comparative experiment of the above methods on the dataset. The experiment demonstrates that the machine learning method performs better than the deep learning model mentioned above on a small number of samples. We find random forest and XGBoost are more efficient than other methods to extract information between variables on postoperative anesthesia recovery time.

Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians

“…A drawback of decision trees is that they tend to overfit, which means that they will perform less well on new data. However, if a collection of many decision trees exists, a technique called random forest can make predictions more stable without overfitting [31][32][33][34][35]. Random forests utilize bootstrap aggregating (bagging) to combine multiple decision trees, resulting in higher accuracy by reducing bias from overfitting datasets with a single model (Figure 6) [21,25,[33][34][35][36].…”

“…However, if a collection of many decision trees exists, a technique called random forest can make predictions more stable without overfitting [31][32][33][34][35]. Random forests utilize bootstrap aggregating (bagging) to combine multiple decision trees, resulting in higher accuracy by reducing bias from overfitting datasets with a single model (Figure 6) [21,25,[33][34][35][36]. Ensemble techniques have become increasingly popular for ML applications because they improve accuracy and reduce variance [21,25].…”