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

ABSTRACT:

Background

Intraoperative physiological monitoring generates a large quantity of time-series data that might be associated with postoperative outcomes. Using a deep learning model based on intraoperative time-series monitoring data to predict postoperative quality of recovery has not been previously reported.

Methods

Perioperative data from female patients having laparoscopic hysterectomy were prospectively collected. Deep learning, logistic regression, support vector machine, and random forest models were trained using different datasets and evaluated by 5-fold cross-validation. The quality of recovery on postoperative day 1 was assessed using the Quality of Recovery-15 scale. The quality of recovery was dichotomized into satisfactory if the score ≥122 and unsatisfactory if

SUBMITTER: Zhao X 

PROVIDER: S-EPMC8022389 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature