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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. [ABSTRACT FROM AUTHOR]

In this study, the authors present the research results on the robust investigate the robust l2–l∞ filtering for Markovian jump linear systems with multiple sensor faults, uncertain probability transition matrix and time‐varying delays. The multiple sensor faults are modelled as multiple independent Bernoulli processes with constant probabilities. The uncertain probability transition matrix is modelled via the polytopic uncertainties for each row in the transition matrix. By using the augmentation method, the filtering error system with stochastic variables is derived. Since of the stochastic variables, the traditional stability condition is not qualified for the analysis of the filtering error systems. Thus, the exponentially mean‐square stability and the robust l2–l∞ performance are adopted for the filtering error system. By choosing the Lyapunov‐based method, sufficient conditions which can guarantee the exponentially mean‐square stability and the robust l2–l∞ performance are obtained in the forms of matrix inequalities. Based on these conditions, the filter design method is proposed and the estimator parameters can be obtained by solving a set of linear matrix inequalities. Finally, a numerical example with two modes is used to show the design procedure and the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]