Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy.

ABSTRACT: Objective: The study aims to develop a mechanical learning model as a predictive model for predicting the appearance of sepsis-associated encephalopathy (SAE). Materials and Methods: The prediction model was developed in a primary cohort of 2,028 sepsis patients from June 2001 to October 2012, retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database. Least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. The model was developed using multivariable logistic regression analysis. The performance of the nomogram has been evaluated in terms of calibration, discrimination, and clinical utility. Results: There were nine particular features in septic patients that were sig

SUBMITTER: Zhao L 

PROVIDER: S-EPMC8636425 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature