Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.

ABSTRACT:

Background

Accurate prediction of thyroidectomy complications is necessary to inform treatment decisions. Ensemble machine learning provides one approach to improve prediction.

Methods

We applied the Super Learner (SL) algorithm to the 2016-2018 thyroidectomy-specific NSQIP database to predict complications following thyroidectomy. Cross-validation was used to assess model discrimination and precision.

Results

For the 17,987 patients undergoing thyroidectomy, rates of recurrent laryngeal nerve injury, post-operative hypocalcemia prior to discharge or within 30 days, and neck hematoma were 6.1%, 6.4%, 9.0%, and 1.8%, respectively. SL improved prediction of thyroidectomy-specific outcomes when compared with benchmark logistic regression approaches. For postoperative hyp

SUBMITTER: Seib CD 

PROVIDER: S-EPMC8172667 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature