1. Construction of prognosis prediction model and visualization system of acute paraquat poisoning based on improved machine learning model
- Author
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Long Li, Xinxuan Han, Zhigang Zhang, Tingyong Han, Peng Wu, Yisha Xu, Liangjie Zhang, Zhenyi Liu, Zhenzhong Xi, Haoran Li, Xiangjiang Yu, Pan He, and Ming Zhang
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Objective This study aims to develop a prognosis prediction model and visualization system for acute paraquat poisoning based on an improved machine learning model. Methods 101 patients with acute paraquat poisoning admitted to 6 hospitals from March 2020 to March 2022 were selected for this study. After expiry of the treatment period (one year of follow-up for survivors and up to the time of death for deceased patients) and they were categorized into the survival group (n = 37) and death group (n = 64). The biochemical indexes of the patients were analyzed, and a prognosis prediction model was constructed using HHO-XGBoost, an improved machine-learning algorithm. Multivariate logistic analysis was used to verify the value of the self-screening features in the model. Results Seven features were selected in the HHO-XGBoost model, including oral dose, serum creatinine, alanine aminotransferase (ALT), white blood cell (WBC) count, neutrophil count, urea nitrogen level, and thrombin time. Univariate analysis showed statistically significant differences between these features’ survival and death groups (P
- Published
- 2024
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