# Prediction of Blood Pressure after Induction of Anesthesia Using Deep Learning: A Feasibility Study
## 2 Materials and Methods  
This paper aims at solving a new problem that predicts future blood pressures in real time. We basically follow Data Science (DS) methodology from problem to approach. As mentioned so far, real-time prediction of blood pressures will help to prevent patients from falling into critical condition. Practically, we follow Cross Industry Standard Process for Data Mining (CRISP-DM) methodology that is an iterative process of several steps such as business understanding, data understanding, data preparation, modeling, evaluation, and deployment. We collect and examine the data of vital signs, and preprocess the data to feed them to train our proposed model as shown in Figure 1. The model is designed to incorporate underlying sequential patterns of the vital signs, and evaluated by averaged absolute errors of 10-fold cross-validation. At the running phase, future blood pressures will be predicted given the vital signs for previous few minutes (e.g., 3 min). As this paper is a feasibility study, it is not ready for deployment; it must be carefully deployed because this is a life and death situation. We will keep collecting more data and improving the model for deployment. Figure 1: Entire process from data acquisition to output visualization, where the solid line represents the training phase and the dotted line corresponds to the running phase. This retrospective study was approved by the institutional review board of Soonchunhyang University Bucheon hospital (approval No. 2019-08-016). We collect data from three operation rooms of Soonchunhyang University Bucheon Hospital, where the operations are performed between 29 October 2018 and 18 January 2019. The data are obtained from various devices using Vital Recorder: B \(\times\) 50 (patient monitor), Solar 8000M (patient monitor), Datex-Ohmeda (anesthesia machine), Primus (anesthesia machine), BIS (brain monitor) and Orchestra (infusion pump), which results in a \(K\)-dimensional real-valued vector. As the vector has few missing values, we employ two strategies: (1) replacing the missing values with the mean of surrounding values, and (2) replacing the missing values with the lastly observed previous value. We apply the first strategy to the vital signs obtainable from the BIS (e.g., signal quality index (SQI)), and make use of the second strategy to other values. In this paper, the vector dimension \(K\) is 27, and the detail of the vital signs is described in Table 1. Each dimension of the vector has a distinct sampling rate; for example, BIS/SQI and BIS/BIS are collected every second, whereas TV and MV are collected every six seconds. To address this issue, we assume that all dimensions have the common sampling rate (i.e., three seconds). For example, the blood pressure values (e.g., mean blood pressure (MBP), systolic blood pressure (SBP), diastolic blood pressure (DBP)) are obtained every 1\(\sim\)3 min (mostly every minute), so these values are assumed to be fixed until their new values come in. That is, if the MBP value is sampled every minute, then the MBP values for every 20 timesteps will be the same. For each \(r\)-th surgery operation, we collect \(K\)-dimensional vectors for \(T_{r}\) seconds, where \(1\leq r\leq R\) and \(R\) denotes the number of operations. As we assume that all vital signs are sampled every three seconds, the total data becomes \(R\times K\times(T_{r}/3)\) tensor (i.e., \(R\) sequence of \(K\times(T_{r}/3)\) matrices). Please note that the \(T_{r}\) for different operation will be different because different operations probably have different operation time. For our collected data, the number of patients (i.e., the number of operations) \(R\) is 102.The statistics of the collected data are summarized in Table 2. Figure 2 depicts a sample sequence of the collected data. Please note that the three blood pressure values are fixed for 20 timesteps (i.e., one minute) while other values change. Figure 2: Sample sequence of real-valued vectors.