Predication of the risk of anaphylaxis during the general anesthesia

DOI Web Site Open Access

Bibliographic Information

Other Title
  • 全身麻酔によるアナフィラキシー様反応のリスク予測

Description

<p>In present study, we aimed to investigate the feasibility of machine-learning-based classification using clinical features of patients for risk predication of anesthesia-related anaphylaxis. </p><p>After data pre-processing, the performance of four classification methods, which were integrated with four feature selection methods, were evaluated using two-layer cross-validation. Linear Discriminate Analysis in conjunction with Recursive Feature Elimination presented the best performance, with accuracy of 0.867 and Matthews correlation coefficient of 0.558 with 25 features used in the classification.</p><p>This study presents initial proof of the capability of a machine-learning-based strategy for forecasting low-prevalence anesthesia-related anaphylaxis. In future, we plan to utilize an extended database including preoperative information and vital-sign streams to define personalized risk status for anaphylaxis.</p>

Journal

Keywords

Details 詳細情報について

Report a problem

Back to top