Dataset Information

HuiyiLi2024 - Prediction of death risk in patients with acute diquat poisoning using Random Forest Model

ABSTRACT: The objective of this study was to develop and validate predictive models to assess the risk of death in patients with acute diquat (DQ) poisoning using innovative machine learning techniques. The study analyzed data from 201 consecutive patients admitted for deliberate oral intake of DQ between February 2018 and August 2023 at the First Hospital and Shengjing Hospital of China Medical University. Initial clinical data were collected, and four machine learning methods—logistic regression, random forest, support vector machine (SVM), and gradient boosting—were applied to build the prediction models. The dataset was split into a training set and a test set in an 8:2 ratio. The performance of these models was evaluated in terms of discrimination, calibration, and clinical decision curve analysis (DCA). Additionally, the SHapley Additive ExPlanations (SHAP) interpretation tool was used to provide an intuitive explanation of the risk of death in patients with DQ poisoning by calculating the contribution and impact of each feature on the final prediction. The areas under the receiver operating characteristic curves (AUCs) for the models were 0.91 for logistic regression, 0.98 for random forest, 0.96 for SVM, and 0.94 for gradient boosting. The random forest model demonstrated the best predictive performance with the highest AUC of 0.98, highest F1-score (0.90), highest Matthews correlation coefficient (0.79), highest accuracy (0.90), and the lowest Brier score (0.07). The study concludes that these machine learning models, combined with SHAP, provide reliable and interpretable tools for predicting the death risk in patients with acute DQ poisoning, with the random forest model being identified as the best performing model.

SUBMITTER: Akshat Pandey

PROVIDER: MODEL2407180001 | BioModels | 2024-07-18

REPOSITORIES: BioModels

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Dataset's files

Source:

			Action	DRS
	MODEL2407180001?filename=Evaluation%20metrics.pdf	Pdf
	MODEL2407180001?filename=README.md	Other
	MODEL2407180001?filename=docker_curves.zip	Other
	MODEL2407180001?filename=docker_train_predict_metrics.zip	Other
	MODEL2407180001?filename=dq_data.xlsx	Xlsx

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Publications

Interpretable machine learning for the prediction of death risk in patients with acute diquat poisoning.

Li Huiyi H Liu Zheng Z Sun Wenming W Li Tiegang T Dong Xuesong X

Scientific reports 20240712 1

The aim of this study was to develop and validate predictive models for assessing the risk of death in patients with acute diquat (DQ) poisoning using innovative machine learning techniques. Additionally, predictive models were evaluated through the application of SHapley Additive ExPlanations (SHAP). A total of 201 consecutive patients from the emergency departments of the First Hospital and Shengjing Hospital of China Medical University admitted for deliberate oral intake of DQ from February 2 ...[more]

PMID: 38997450

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