Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database.

Journal: Lupus science & medicine

PMID: 39947742

Abstract

OBJECTIVE: Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive models for the mortality risk.

Authors

Zhihan Chen

Department of Rheumatology and Immunology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, 134 Dongjie Road, Fuzhou, 350001, Fujian Province, China. zhihanchen213@163.com.
Yunfeng Dai

Department of Rheumatology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Yilin Chen

College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
Han Chen

School of Statistics, University of Minnesota at Twin Cities.
Huiping Wu

School of Mathematics and Statistics, Fujian Normal University, Fuzhou, China hpwu168@163.com lilydoctor9902@163.com.
Li Zhang

Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.

Keywords

Adult Area Under Curve Critical Illness Databases, Factual Female Humans Logistic Models Lupus Erythematosus, Systemic Machine Learning Male Middle Aged Nomograms Risk Assessment Risk Factors ROC Curve

External Resources

View on PubMed Access via DOI PubMed (39947742)

Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database.

Abstract

Authors

Keywords

External Resources

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