Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Dec 7;30(3):344-352.
doi: 10.3171/2018.8.SPINE1869. Print 2019 Mar 1.

Machine learning modeling for predicting hospital readmission following lumbar laminectomy

Saisanjana Kalagara  1 , Adam E M Eltorai  1 , Wesley M Durand  1 , J Mason DePasse  1 , Alan H Daniels  1   2
Affiliations

Machine learning modeling for predicting hospital readmission following lumbar laminectomy

Saisanjana Kalagara et al. J Neurosurg Spine. .

Abstract

In BriefAuthors of this study analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy >95% when using all variables and >79% when using only predischarge variables. A model capable of predicting 40% of readmitted patients was created using only the variables known predischarge. This investigation is important in its provision of data that will assist the development of predictive models for readmission as well as interventions to prevent readmission in high-risk patients.

Keywords: ASA = American Society of Anesthesiologists; AUC = area under the receiver operating characteristic curve; BMI = body mass index; GBM = gradient boosting machine; LOS = length of stay; RVU = relative value unit; SMOTE = Synthetic Minority Oversampling Technique; diagnostic technique; hospital readmission; lumbar; machine learning; predictive model; spine surgery.

PubMed Disclaimer

LinkOut - more resources