Predicting in-hospital mortality for MIMIC-III patients: A nomogram combined with SOFA score

ABSTRACT: Predicting the mortality of patients provides a reference for doctors to judge their physical condition. This study aimed to construct a nomogram to improve the prediction accuracy of patients’ mortality. Patients with severe diseases were screened from the Medical Information Mart for Intensive Care (MIMIC) III database; 70% of patients were randomly selected as the training set for the model establishment, while 30% were used as the test set. The least absolute shrinkage and selection operator (LASSO) regression method was used to filter variables and select predictors. A multivariable logistic regression fit was used to determine the association between in-hospital mortality and risk factors and to construct a nomogram. A total of 9276 patients were included. The area under the curve (A

SUBMITTER: Liu R 

PROVIDER: S-EPMC9592355 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature