prediction of pralidoxime dose in patients with organophosphate poisoning using machine learning techniques

Background: early and appropriate antidotal therapy is crucial for patients with organophosphate poisoning. objectives: given the lack of a comprehensive consensus on the optimal dose of pralidoxime for patients with organophosphate poisoning, this study aims to develop a machine learning-based prediction model to determine the individualized pralidoxime dose for these patients. methods: the dataset was divided into training and test sets with a 70:30 ratio. feature selection was conducted using pearson’s correlation coefficient (filter approach) method. both classification and regression were employed to develop the prediction model using the selected features. the performance of the developed models was evaluated using ten-fold cross-validation and various metrics, including sensitivity, specificity, accuracy, f1-score, and auc. the models were implemented and assessed using the scikit-learn library in python. results: after applying exclusion criteria, data from 325 patients were utilized to train and test the machine-learning models. in the classification approach, the random forest method achieved superior performance with an auc of 98.6. in the regression approach, the gradient boosting regressor, with an r 2value of 65.4, outperformed other algorithms. feature selection revealed that muscular weakness, plasma cholinesterase activity, and blood urea nitrogen were the most significant predictors of pralidoxime dose in the classification model. in the regression model, the top predictors were age, hco 3-vbg, and atropine bolus. many of the selected features coincide with those identified in previous studies, with muscular weakness being particularly significant in both models. conclusions: the most effective algorithms could be employed to develop a clinical decision support system for personalized pralidoxime dosage prediction in patients with organophosphorus poisoning. however, the study is constrained by its small sample size, retrospective design, and the absence of an external validation cohort. conducting a prospective multicenter study with a larger sample size is crucial to validate the findings of this study.