title: An Effective Machine Learning Approach for Prognosis of Paraquat Poisoning Patients Using Blood Routine Indexes.
abstract: The early identification of toxic paraquat (PQ) poisoning in patients is critical to ensure timely and accurate prognosis. Although 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 data set to determine its accuracy, sensitivity and specificity. Feature selection was also applied to identify the factors correlated with risk status, and the results showed that there are significant differences in blood routine indexes between dead and living PQ-poisoned individuals (p-value < 0.01). Feature selection also showed that the most important correlated indexes are white blood cell and neutrophils. In conclusion, the toxicity or prognosis of PQ poisoning can be preliminarily ascertained by blood routine testing without PQ concentration data, representing an additional tool and innovative approach to assess the prognosis of PQ poisoning.
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, China.
authors.forename: Huiling
authors.initials: H
authors.lastname: Chen
authors.affiliation: Department of Pharmacy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
authors.forename: Lufeng
authors.initials: L
authors.lastname: Hu
authors.affiliation: Department of Computing, Lishui University, Lishui, 323000, Zhejiang, P. R. China.
authors.forename: Huaizhong
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
authors.forename: Guangliang
authors.initials: G
authors.lastname: Hong
authors.affiliation: Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.|Department of Intensive Care Unit, Lishui Central Hospital, Lishui, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Analytical and Testing Center, Wenzhou Medical University, Wenzhou, China.
authors.forename: Jianshe
authors.initials: J
authors.lastname: Ma
authors.affiliation: Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
authors.forename: Zhongqiu
authors.initials: Z
authors.lastname: Lu
mesh_terms: D000328:Adult; D000465:Algorithms; D002681:China; D003936:Diagnosis, Computer-Assisted; D042241:Early Diagnosis; D005260:Female; D006540:Herbicides / Q000633:toxicity*; D006784:Hospitals, Teaching; D006801:Humans; D007958:Leukocyte Count; D000069550:Machine Learning; D008297:Male; D008954:Models, Biological; D009504:Neutrophils / Q000187:drug effects; D010269:Paraquat / Q000633:toxicity*; D011041:Poisoning / Q000097:blood / Q000175:diagnosis* / Q000503:physiopathology / Q000628:therapy; D011379:Prognosis; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index
chemical_list: D006540:Herbicides; D010269:Paraquat