Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury.

ABSTRACT:

Aims

Hypokalemia is a common complication following traumatic brain injury, which may complicate treatment and lead to unfavorable outcomes. Identifying patients at risk of hypokalemia on the first day of admission helps to implement prophylactic treatment, reduce complications, and improve prognosis.

Methods

This multicenter retrospective study was performed between January 2017 and December 2020 using the electronic medical records of patients admitted due to traumatic brain injury. A propensity score matching approach was adopted with a ratio of 1:1 to overcome overfitting and data imbalance during subgroup analyses. Five machine learning algorithms were applied to generate a best-performed prediction model for in-hospital hypokalemia. The internal fivefold cross-validati

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC9804086 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature