Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.
- VernacularTitle:基于机器学习的脓毒症死亡率预测模型对比研究
- Author:
Zi-Yang WANG
1
;
Yu-Shan LAN
1
;
Zi-Du XU
1
;
Yao-Wen GU
1
;
Jiao LI
1
Author Information
- Publication Type:Journal Article
- Keywords: MIMIC-IV; machine learning; risk prediction; sepsis
- MeSH: Humans; Machine Learning; Sepsis; Logistic Models
- From: Chinese Medical Sciences Journal 2022;37(3):201-209
- CountryChina
- Language:English
- Abstract: Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical features were generated and selected by mutual information and grid search. Logistic regression, Random forest, LightGBM, XGBoost, and other machine learning models were constructed to predict the mortality probability. Five measurements including accuracy, precision, recall, F1 score, and area under curve (AUC) were acquired for model evaluation. An external validation was implemented to avoid conclusion bias. Results LightGBM outperformed other methods, achieving the highest AUC (0.900), accuracy (0.808), and precision (0.559). All machine learning models performed better than SAPS II score (AUC=0.748). LightGBM achieved 0.883 in AUC in the external data validation. Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS II score.
