Continuous real-time prediction of surgical case duration using a modular artificial neural network.

ABSTRACT:

Background

Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration.

Methods

Preoperative (e.g. procedure name) and intraoperative (e.g. medications and vital signs) variables were retrieved from anaesthetic records of surgeries performed between March 1, 2019 and October 31, 2019. A modular artificial neural network was developed and compared with a Bayesian approach and the scheduled surgical duration. Continuous ranked probability score (CRPS) was used as a measure of time error to assess model accuracy. For evaluating clinical performance, accuracy for each approach was assesse

SUBMITTER: Jiao Y 

PROVIDER: S-EPMC9074795 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature