A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy.

ABSTRACT:

Study design

A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC).

Purpose

To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose.

Overview of literature

Based on our literature review, no study has examined the capability of ML algorithms to predict major shortterm ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications.

Methods

The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML mo

SUBMITTER: Karabacak M 

PROVIDER: S-EPMC11366553 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature