关键词: MIMIC-IV, 脓毒血症, 机器学习, 风险预测

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.

Key words: MIMIC-IV, sepsis, machine learning, risk prediction

基金资助: 中国医学科学院“医学知识管理与智能化知识服务关键技术研究”(2021-I2M-1-056);中国医学科学院“医学人工智能技术与人机交互关键问题研究”(2018-I2M-AI-016);中国国家重点研发计划“精准医学本体和语义网络构建”(2016YFC0901901);中国国家重点研发计划“中国人群多组学参比数据库系统研发”(2017YFC0907503)