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Table 1 Type and nature of the data used in modeling

From: Can a deep learning model based on intraoperative time-series monitoring data predict post-hysterectomy quality of recovery?

Type of data Nature of data
Preoperative data
 Demographics (Age, height, body weight, and BMI) Numerical data
 ASA physical status classification Categorical data
 Anesthesia-relevant history (general anesthesia, spinal anesthesia, nerve block or local anesthesia, postoperative nausea and vomiting, and motor sickness) Categorical data
 Comorbidities (psychiatric disease, neurologic disease, hypertension, cardiovascular disease, pulmonary disease, endocrinologic disease, renal insufficiency, and digestive disease) Categorical data
 Laboratory results (hemoglobin, hematocrit, and creatinine) Numerical data
Intraoperative intervention dataa
 Anesthetic time Numerical data
 Propofol, remifentanil, and sufentanilb Numerical data
 Crystalloid, urine output, and blood loss Numerical data
Intraoperative monitoring data
 Respiratory rate and end-tidal carbon dioxide Time-series data, captured from anesthesia machine
 Heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse oxygen saturation and body temperature Time-series data, captured from vital sign monitors
 Muscular tissue oxygen saturation Time-series data, captured from NIRS-based tissue oximeter
  1. BMI body mass index, ASA American Society of Anesthesiologists, NIRS near-infrared spectroscopy, iMODIPONV trial the intervention guided by Muscular Oxygenation to Decrease the Incidence of PostOperative Nausea and Vomiting (iMODIPONV) trial
  2. aFor the intraoperative intervention data, the totals of different variables for the entire surgery were used in modeling
  3. bSufentanil was the standardized opioid for pain control in the iMODIPONV trial