首页|Machine Learning-Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation
Machine Learning-Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation
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NSTL
BACKGROUND; Unplanned readmission after transsphenoidal resection of pituitary adenoma can occur in up to 10% of patients but is unpredictable。 OBJECTIVE: To develop a reliable system for predicting unplanned readmission and create a validated method for stratifying patients by risk。 METHODS: Data sets were retrospectively collected from the National Surgical Quality Improvement Program and 2 tertiary academic medical centers。 Eight machine learning classifiers were fit to the National Surgical Quality Improvement Program data， optimized using Bayesian parameter optimization and evaluated on the external data。 Permutation analysis identified the relative importance of predictive variables， and a risk stratification system was built using the trained machine learning models。
Brendan T. Crabb、Forrest Hamrick、Justin M. Campbell、Joshua Vignolles-Jeong、Stephen T. Magill、Daniel M. Prevedello、Ricardo L. Carrau、Bradley A. Otto、Douglas A. Hardesty、William T. Couldwell、Michael Karsy
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Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
NSTL
