Predictive modeling of ambulatory outcomes after spinal cord injury using machine learning.

ABSTRACT:

Study design

Retrospective multi-site cohort study.

Objectives

To develop an accurate machine learning predictive model using predictor variables from the acute rehabilitation period to determine ambulatory status in spinal cord injury (SCI) one year post injury.

Setting

Model SCI System (SCIMS) database between January 2000 and May 2019.

Methods

Retrospective cohort study using data that were previously collected as part of the SCI Model System (SCIMS) database. A total of 4523 patients were analyzed comparing traditional models (van Middendorp and Hicks) compared to machine learning algorithms including Elastic Net Penalized Logistic Regression (ENPLR), Gradient Boosted Machine (GBM), and Artificial Neural Networks (ANN).

Results

Compared with GBM and

SUBMITTER: Draganich C 

PROVIDER: S-EPMC12057807 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature