An Effective Machine Learning Approach for Prognosis of Paraquat Poisoning Patients Using Blood Routine Indexes. - PubMed - NCBI

The early identification of toxic paraquat (PQ) poisoning in patients critical to ensure timely and accurate prognosis. Though plasma PQ concentration has been reported as a clinical indicator of PQ poisoning, it is not commonly applied in practice due to the inconvenient necessary instruments and operation. In this study, we explored the use of blood routine indexes to identify the degree of PQ toxicity and/or diagnose PQ poisoning in patients via machine learning approach. Specifically, we developed a method based on support vector machine combined with the feature selection technique to accurately predict PQ poisoning risk status, then tested the method on 79 (42 male and 37 female; 41 living and 38 deceased) patients. The detection method was rigorously evaluated against a real-world dataset to determine its accuracy, sensitivity and specificity. Feature selection was also applied to identify factors correlated with risk status, and results showed that there are significant differences in blood routine indexes between dead and living PQ-poisoned individuals (p-value 0.01).