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Machine learning models to predict length of stay and discharge destination in complex head and neck surgery
Author(s) -
Goshtasbi Khodayar,
Yasaka Tyler M.,
ZandiToghani Mehdi,
Djalilian Hamid R.,
Armstrong William B.,
Tjoa Tjoson,
Haidar Yarah M.,
Abouzari Mehdi
Publication year - 2021
Publication title -
head and neck
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.012
H-Index - 127
eISSN - 1097-0347
pISSN - 1043-3074
DOI - 10.1002/hed.26528
Subject(s) - gradient boosting , receiver operating characteristic , head and neck , machine learning , medicine , boosting (machine learning) , regression , artificial intelligence , mean squared error , computer science , surgery , laryngectomy , statistics , mathematics , random forest , larynx
This study develops machine learning (ML) algorithms that use preoperative-only features to predict discharge-to-nonhome-facility (DNHF) and length-of-stay (LOS) following complex head and neck surgeries.