# Prediction of Blood Pressure after Induction of Anesthesia Using Deep Learning: A Feasibility Study
## 1 Introduction  
General anesthesia for surgery can be divided into three phases (i.e., induction, maintenance and emergence from anesthesia). Especially in the anesthesia-induction phase, the changes in blood pressure is rapid and can range variously from hypotension to hypertension. This usually caused not only by the administration of intravenous anesthetic agents (propofol and remifentanil), volatile anesthetic agents, neuromuscular blocking agents but also by airway manipulation to intubate patient's trachea for the mechanical ventilation. It has been reported that hypotension, even a short duration of mean arterial pressure less than 55 mmHg, is associated with acute kidney injury and myocardial injury [1]. On the other hand, hypertension, if left untreated, increases risks of bleeding, cerebrovascular events, and myocardial infarction postoperatively [2]. If blood pressure can be accurately predicted, anesthesiologist may proactively search for possible causes to prevent severe hemodynamic changes. This may enable early intervention such as adjustment of anesthetic agents, fluids, and vasoactive drugs. Thus, patient may not experience harmful consequences caused by hypotension or hypertension as such events can be prevented ahead. However, intraoperative accurate blood pressure predictions is nearly impossible because of complex mechanisms that cause blood pressure changes, and at least similar predictions require much experience and knowledge of factors that affect blood pressure fluctuations during surgery. The volume of modern anesthesia data has increased with the use of electronic medical records. It is difficult for anesthesiologist to use these data to judge patients hemodynamic status during operation, so it will be helpful if there is a tool to support clinical decision making for anesthesiologist based on these data. Machine learning technique is one of the tools for supporting clinical decision making, as it is known to be effective in learning arbitrary patterns of data. There have been few studies on hypotension prediction using machine learning models. One study used various machine learning models based on existing information in the electronic health records to predict hypotension within 10 min of anesthesia induction [3]. It should be noted that it does not predict when exactly the hypotension event will occur, but predicts whether the event will occur within 10 min; so it is not applicable to real-time service because the event may occur right after 1-2 s. There was another study that predicts the probability of developing hypotension 15 min before its actual occurrence by applying a machine learning model to the waveform of invasive arterial blood pressure [4]. Although it predicts the potential hypotension event before its actual occurrence, it is not suitable to real-time prediction because it assumes that the hypotension event does not occur again within 20 min. It is obvious that we do not know when future events will occur, so it is impossible to use it for real-time prediction based on such assumption. As far as we know, most previous studies basically aimed at a classification problem (i.e., predict the potential event), but there was no study for a real-time regression problem (i.e., predict the actual blood pressure). Machine learning models have been widely adopted for the regression problem. Support vector regression (SVR) [5], which is based on the same principles of the support vector machine (SVM) [6], has been used to predict various real-numbers (e.g., stock price, demand/supply of pulpwood) [7; 8]. Random forest (RF) [9] is one of ensemble learning methods. It is known to keep low bias of decision trees and avoid overfitting by controlled variance. The RF can be used for either classification or regression [10]. These traditional machine learning models have shown quite successful results, but they suffer from a common limitation; they strongly depend on a hand-crafted feature set that requires much effort of experts.