Ziyang Wang , Yushan Lan , Zidu Xu, Yaowen Gu, Jiao Li
{"title":"Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning","authors":"Ziyang Wang , Yushan Lan , Zidu Xu, Yaowen Gu, Jiao Li","doi":"10.24920/004102","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>LightGBM outperformed other methods, achieving the highest AUC (0.900), accuracy (0.808), and precision (0.SS9). All machine learning models performed better than SAPS II score (AUC=0.748). LightGBM achieved 0.883 in AUC in the external data validation.</p></div><div><h3>Conclusions</h3><p>The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS II score.</p></div>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Medical Sciences Journal","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S100192942200044X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
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.SS9). 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.