Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery.

ABSTRACT:

Background

Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort.

Methods

We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning al

SUBMITTER: Vollmer A 

PROVIDER: S-EPMC10623164 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature