Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves' disease after radioiodine therapy.

ABSTRACT:

Background and objective

Radioiodine therapy (RAI) is one of the most common treatment solutions for Graves' disease (GD). However, many patients will develop hypothyroidism as early as 6 months after RAI. This study aimed to implement machine learning (ML) algorithms for the early prediction of post-RAI hypothyroidism.

Methods

Four hundred and seventy-one GD patients who underwent RAI between January 2016 and June 2019 were retrospectively recruited and randomly split into the training set (310 patients) and the validation set (161 patients). These patients were followed for 6 months after RAI. A set of 138 clinical and lab test features from the electronic medical record (EMR) were extracted, and multiple ML algorithms were conducted to identify the features associated wit

SUBMITTER: Duan L 

PROVIDER: S-EPMC9175589 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature