Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).

ABSTRACT:

Background

Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model's predictive value for these patients.

Methods

Clinical information of elderly patients with sepsis in MIMIC IV database was collected retrospectively. Machine learning (RSF) was used to select the top 30 variables in the training cohort to build the final RSF model. The model was compared with the traditional scoring systems SOFA, SAPSII, and APSIII. The performance of the mode

SUBMITTER: Zhang L 

PROVIDER: S-EPMC8832779 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature