Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry.
J Neurosurg Spine
; : 1-11, 2019 Jun 07.
Article
en En
| MEDLINE
| ID: mdl-31174185
ACC = accuracy; ACS-NSQIP = American College of Surgeons National Surgical Quality Improvement Program; ALP = alkaline phosphatase; ANN = artificial neural network; ASA = American Society of Anesthesiologists; AUC = area under the receiver operating characteristic curve; BUN = blood urea nitrogen; Bayes theorem; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; CPT = Current Procedural Terminology; GBM = gradient boosting machine; GLM = generalized linear model; GLMnet = elastic-net GLM; HTN = hypertension; INR = international normalized ratio; LASSO = least absolute shrinkage and selection operator; NPV = negative predictive value; NSQIP; NSQIP = National Surgical Quality Improvement Program; ODI = Oswestry Disability Index; PHC = predictive hierarchical clustering; PPV = positive predictive value; PTT = partial thromboplastin time; RF = random forest; ROC = receiver operating characteristic; SGOT = serum glutamic oxaloacetic transaminase; WBC = white blood cell count; cervical; discharge; elastic net; generalized linear model; gradient boosting machines; logistic regression; lumbar; machine learning; neural networks; outcomes; pLDA = penalized linear discriminant analysis; penalized discriminant analysis; predictive modeling; random forest; rehabilitation; skilled nursing facility; spinal fusion; spine surgery; unplanned readmission
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
J Neurosurg Spine
Asunto de la revista:
NEUROCIRURGIA
Año:
2019
Tipo del documento:
Article
Pais de publicación:
Estados Unidos