title: An intelligent prognostic system for analyzing patients with paraquat poisoning using arterial blood gas indexes.
abstract: The arterial blood gas (ABG) test is used to assess gas exchange in the lung, and the acid-base level in the blood. However, it is still unclear whether or not ABG test indexes correlate with paraquat (PQ) poisoning. This study investigates the predictive value of ABG tests in prognosing patients with PQ poisoning; it also identifies the most significant indexes of the ABG test. An intelligent machine learning-based system was established to effectively give prognostic analysis of patients with PQ poisoning based on ABG indexes. In the proposed system, an enhanced support vector machine combined with a feature selection strategy was developed to predict the risk status from a pool of 103 patients (56 males and 47 females); of these, 52 subjects were deceased and 51 patients were alive. The proposed method was rigorously evaluated against the real-life dataset in terms of accuracy, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for the risk status. The results demonstrated that there were significant differences in ABG indexes between deceased and alive subjects (p-value <0.01). According to the feature selection, we found that the most important correlated indexes were associated with partial pressure of carbon dioxide (PCO2). This study discovered the relationship between ABG test and poisoning degree to provide a new avenue for prognosing PQ poisoning.
authors.affiliation: Department of Pharmacy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
authors.forename: Lufeng
authors.initials: L
authors.lastname: Hu
authors.affiliation: Centre Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
authors.forename: Feiyan
authors.initials: F
authors.lastname: Lin
authors.affiliation: Department of Computing, Lishui University, Lishui 323000, Zhejiang, China.
authors.forename: Huaizhong
authors.initials: H
authors.lastname: Li
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Changfei
authors.initials: C
authors.lastname: Tong
authors.affiliation: Information Technology Center, Wenzhou Medical University, 325035, China.
authors.forename: Zhifang
authors.initials: Z
authors.lastname: Pan
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Li
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China. Electronic address: chenhuiling.jlu@gmail.com.
authors.forename: Huiling
authors.initials: H
authors.lastname: Chen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001784:Blood Gas Analysis / Q000379:methods; D005260:Female; D006540:Herbicides / Q000097:blood* / Q000506:poisoning*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods; D010269:Paraquat / Q000097:blood* / Q000506:poisoning*; D011379:Prognosis; D055815:Young Adult
chemical_list: D006540:Herbicides; D010269:Paraquat
keywords: Arterial blood gas; Outcome prediction; Paraquat; Particle swarm optimization; Prognostic system; Support vector machine