Machine learning modeling for predicting hospital readmission following lumbar laminectomy.
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
Journal
Journal of neurosurgery. Spine
ISSN: 1547-5646
Titre abrégé: J Neurosurg Spine
Pays: United States
ID NLM: 101223545
Informations de publication
Date de publication:
01 03 2019
01 03 2019
Historique:
received:
14
03
2018
accepted:
15
08
2018
pubmed:
14
12
2018
medline:
18
10
2019
entrez:
15
12
2018
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 30544346
doi: 10.3171/2018.8.SPINE1869
pii: 2018.8.SPINE1869
doi:
pii:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM