title: Neural network modeling to predict the hypnotic effect of propofol bolus induction.
abstract: Dose requirements of propofol to achieve loss of consciousness depend on the interindividual variability. Until now when propofol was administered by a single bolus, how to define the optimal individual dose and to assess its hypnotic effect have not been clearly studied. The goal of this study is to develop an artificial neural network model to predict the hypnotic effect of propofol on the basis of common clinical parameters. Ten parameters were chosen as the input factors based on the related literatures and clinical experiences. The bispectral index of EEG was used to record the consciousness level of patients and served as the output factor. The predictive results of neural net models were superior to that of clinician. This model could potentially help determine the optimal dose of propofol and thus reduce the anesthetic cost.
authors.affiliation: Department of Anesthesiology, Taipei Medical University Hospital, Taipei, Taiwan.
authors.forename: Chao-Shun
authors.initials: CS
authors.lastname: Lin
authors.forename: Yu-Chuan
authors.initials: YC
authors.lastname: Li
authors.forename: Martin S
authors.initials: MS
authors.lastname: Mok
authors.forename: Chi-Chen
authors.initials: CC
authors.lastname: Wu
authors.forename: Hung-Wen
authors.initials: HW
authors.lastname: Chiu
authors.forename: Yu-Hwa
authors.initials: YH
authors.lastname: Lin
mesh_terms: D000771:Anesthesia, Intravenous; D018686:Anesthetics, Intravenous / Q000008:administration & dosage* / Q000494:pharmacology; D003658:Decision Making, Computer-Assisted; D004569:Electroencephalography / Q000187:drug effects*; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D015742:Propofol / Q000008:administration & dosage* / Q000494:pharmacology; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D018686:Anesthetics, Intravenous; D015742:Propofol