Interpretable machine learning models to predict short-term postoperative outcomes following posterior cervical fusion.

ABSTRACT: By predicting short-term postoperative outcomes before surgery, patients who undergo posterior cervical fusion (PCF) surgery may benefit from more precise patient care plans that reduce the likelihood of unfavorable outcomes. We developed machine learning models for predicting short-term postoperative outcomes and incorporate these models into an open-source web application in this study. The American College of Surgeons National Surgical Quality Improvement Program database was used to identify patients who underwent PCF surgery. Prolonged length of stay, non-home discharges, and readmissions were the three outcomes that were investigated. To predict these three outcomes, machine learning models were developed and incorporated into an open access web application. A total of 6277 patients

SUBMITTER: Karabacak M 

PROVIDER: S-EPMC10361477 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature