Machine Learning Prediction Models for Postoperative Stroke in Elderly Patients: Analyses of the MIMIC Database.

ABSTRACT:

Objective

With the aging of populations and the high prevalence of stroke, postoperative stroke has become a growing concern. This study aimed to establish a prediction model and assess the risk factors for stroke in elderly patients during the postoperative period.

Methods

ML (Machine learning) prediction models were applied to elderly patients from the MIMIC (Medical Information Mart for Intensive Care)-III and MIMIC-VI databases. The SMOTENC (synthetic minority oversampling technique for nominal and continuous data) balancing technique and iterative SVD (Singular Value Decomposition) data imputation method were used to address the problem of category imbalance and missing values, respectively. We analyzed the possible predictive factors of stroke in elderly patients using

SUBMITTER: Zhang X 

PROVIDER: S-EPMC9341133 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature