Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies.

ABSTRACT:

Background

Patients who were diagnosed with hematologic malignancies (HM) had a higher risk of acute kidney injury (AKI). This study applies the Bayesian networks (BNs) to investigate the interrelationships between AKI and its risk factors among HM patients, and to evaluate the predictive and inferential ability of BNs model in different clinical settings.

Methods

During 2014 and 2015, a total of 2501 inpatients with HM were recruited in this retrospective study conducted in a tertiary hospital, Shanghai of China. Patients' demographics, medical history, clinical and laboratory records on admission were extracted from the electronic medical records. Candidate predictors of AKI were screened in the group-LASSO (gLASSO) regression, and then they were incorporated into BNs anal

SUBMITTER: Li Y 

PROVIDER: S-EPMC7201633 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature