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A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices

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  • Lufeng Hu
  • Huaizhong Li
  • Zhennao Cai
  • Feiyan Lin
  • Guangliang Hong
  • Huiling Chen
  • Zhongqiu Lu

Abstract

The prognosis of paraquat (PQ) poisoning is highly correlated to plasma PQ concentration, which has been identified as the most important index in PQ poisoning. This study investigated the predictive value of coagulation, liver, and kidney indices in prognosticating PQ-poisoning patients, when aligned with plasma PQ concentrations. Coagulation, liver, and kidney indices were first analyzed by variance analysis, receiver operating characteristic curves, and Fisher discriminant analysis. Then, a new, intelligent, machine learning-based system was established to effectively provide prognostic analysis of PQ-poisoning patients based on a combination of the aforementioned indices. In the proposed system, an enhanced extreme learning machine wrapped with a grey wolf-optimization 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 alive. The proposed method was rigorously evaluated against this real-life dataset, in terms of accuracy, Matthews correlation coefficients, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for risk status. The results demonstrated that there were significant differences in the coagulation, liver, and kidney indices between deceased and surviving subjects (p

Suggested Citation

  • Lufeng Hu & Huaizhong Li & Zhennao Cai & Feiyan Lin & Guangliang Hong & Huiling Chen & Zhongqiu Lu, 2017. "A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-20, October.
    • Handle: RePEc:plo:pone00:0186427
    DOI: 10.1371/journal.pone.0186427
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    Cited by:

    1. Fan, Yi & Wang, Pengjun & Heidari, Ali Asghar & Chen, Huiling & HamzaTurabieh, & Mafarja, Majdi, 2022. "Random reselection particle swarm optimization for optimal design of solar photovoltaic modules," Energy, Elsevier, vol. 239(PA).
    2. Wen Jie Wang & Li Wei Zhang & Shun Yi Feng & Jie Gao & Yong Li, 2018. "Sequential organ failure assessment in predicting mortality after paraquat poisoning: A meta-analysis," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-12, November.
    3. Chen, Chengcheng & Wang, Xianchang & Yu, Helong & Wang, Mingjing & Chen, Huiling, 2021. "Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 291-318.

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