Machine Learning-Based Model for Individualized Drug Dose Prediction for Propofol

Establishment of a database of clinical characteristics of propofol-induced loss of consciousness in patients with complete clinical information Acquisition of basic perioperative monitoring data and extended monitoring data: 1,000 patients aged ≥18 years who needed to undergo surgical treatment were included (according to the machine learning diagnosis results, if the poor model fit was due to the small sample size, the necessary number of samples could be continued to be collected), and perioperative monitoring and management of the patients was performed, and a video recorder was used to videotape the whole anesthesia induction process in real time, so as to facilitate the postoperative integration of various data. The basic characteristics of the patients and the perioperative monitoring characteristics were extracted from the surgical anesthesia recording system, including gender, age, height, weight, blood pressure, ASA classification, electroencephalographic parameters, the a