TY  - JOUR
AU  - Liu, Yuxuan
AU  - Lyu, Xiaoguang
AU  - Yang, Bo
AU  - Fang, Zhixiang
AU  - Hu, Dejun
AU  - Shi, Lei
AU  - Wu, Bisheng
AU  - Tian, Yong
AU  - Zhang, Enli
AU  - Yang, YuanChao
PY  - 2023
DA  - 2023/3/21
TI  - Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach
JO  - JMIR Form Res
SP  - e44666
VL  - 7
KW  - mushroom poisoning
KW  - triage
KW  - model
KW  - machine learning
KW  - XGBoost
KW  - extreme gradient boosting
AB  - Background: Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data. Objective: The purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies. Methods: In all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations. Results: Among several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians’ assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73). Conclusions: The 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2023/1/e44666
UR  - https://doi.org/10.2196/44666
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36943366
DO  - 10.2196/44666
ID  - info:doi/10.2196/44666
ER  -