Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy
Objective: Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE). Materials and methods: ML models were developed and validated based on a public database named Medical In...
Main Authors: | Cheng, C. (Author), Jin, Z. (Author), Li, W. (Author), Mao, Z. (Author), Peng, C. (Author), Peng, L. (Author), Wang, J. (Author), Yang, F. (Author), Zuo, W. (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
BioMed Central Ltd
2022
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
Development of a nomogram to predict 30-day mortality of patients with sepsis-associated encephalopathy: a retrospective cohort study
by: Yang Yang, et al.
Published: (2020-07-01) -
Sepsis-Associated Encephalopathy: From Delirium to Dementia?
by: Ha-Yeun Chung, et al.
Published: (2020-03-01) -
CLINICAL APPROACH AND GUIDELINES FOR MANAGEMENT OF SEPSIS ASSOCIATED ENCEPHALOPATHY
by: Ayu Trisnawati, et al.
Published: (2019-05-01) -
A retrospective study of sepsis-associated encephalopathy: epidemiology, clinical features and adverse outcomes
by: Jiayi Chen, et al.
Published: (2020-10-01) -
Microglia: A Potential Therapeutic Target for Sepsis-Associated Encephalopathy and Sepsis-Associated Chronic Pain
by: Yi Li, et al.
Published: (2020-11-01)