An efficient machine learning approach for diagnosis of paraquat-poisoned patients
Introduction
Paraquat (1,1′-dimethyl-4,4′-bipyridium dichloride, PQ), the most widely used herbicides in the world, has been deemed as the most highly toxic pesticide for human [1]. Its mortality rate is highly correlated with plasma PQ concentrations [2]. Acute ingesting 7–8 mL PQ can cause serious symptoms such as liver, lung, kidney, and heart failure that directly leads to death if without getting prompt treatment [3]. Although few of PQ poisoning appeared in developed countries, there are thousands of people died of PQ poisoning every year in developing countries [4]. For example, PQ accounts for most fatal poisonings, with 500 or more deaths per year in Korea [5].
PQ intoxication is associated with reactive oxygen species and free radicals that cause early multiorgan failure and late pulmonary fibrosis with respiratory failure [5], [6]. The current treatment strategies of PQ poisoning are increasing the elimination of PQ from the body, administration of antioxidants and the maintenance of vital functions, which is an entirely different from other intervention of intoxication [1], [7]. And earlier the treatment initiated, more effective in reducing mortality, particularly hemoperfusion (HP) within 2–4 h after intoxication [4]. Therefore, diagnosis is very important in treatment of PQ-poisoned patients.
Till now, the diagnosis of PQ poisoning mainly is according to the PQ concentration in blood. However, PQ is absorbed poorly from the stomach and small intestine (< 5%) and distributed into all organs in the body within 5 h, which means it is difficult to detect PQ in blood after poisoned 5 h [1], [8]. And in some cases, the patients cannot provide a clear contact history of PQ poisoning, such as disturbance of consciousness or language. This poses a more serious problem for diagnosis. How to develop a new diagnosis method of PQ poisoning is becoming an important topic in medicine.
In this study, patients with acute PQ intoxication were involved and determined by gas chromatography coupled with mass spectrometry (GC–MS). According to their plasma metabonomics, a rapid diagnosis method was developed based on the extreme learning machine (ELM) technique [9], a new learning algorithm for a single hidden layer feed-forward neural network (SLFNs). Different from the common parameter tuning strategy of neural network, ELM tries to choose input weights and hidden biases randomly, and the output weights are analytically determined by using Moore–Penrose (MP) generalized inverse. It not only learns much faster with higher generalization performance, but also keeping very few parameters for tuning. Thanks to its good properties, ELM has found its applications in a wide range of fields such as cancer diagnosis [10], image quality assessment [11], face recognition [12], land cover classification [13] and hyperspectral images classification [14]. To the best of our knowledge, there is no research work dealing with the problem of PQ poisoning from the machine learning perspective. Therefore, an attempt was made in this study to explore the potential of the performance of ELM in discriminating the PQ-poisoned patients from the healthy controls. For comparison purpose, the support vector machines [15] (SVM) was also taken for diagnosis of PQ poisoning. In addition, the effectiveness of feature selection was investigated as well. The efficient and commonly used feature selection method, maximum relevance minimum redundancy (mRMR) [16], was employed for pre-processing before the classification models were constructed. mRMR is a filter type feature selection method that seeks to choose features which are relevant to the target class (maximum relevance) and come up with the feature subset containing as non-redundant features as possible (minimum redundancy). The effectiveness of the proposed approach is examined in terms of classification accuracy, AUC, sensitivity and specificity respectively on the diagnosis of PQ-poisoned cases whose samples were collected from The First Affiliated Hospital of Wenzhou Medical University. Promisingly, the developed ELM based approach has achieved high diagnosis accuracy, AUC, sensitivity and specificity of 91.64%, 0.9156%, 91.33% and 91.78%, respectively.
The remainder of this paper is organized as follows. Section 2 offers brief background knowledge on ELM. The detail of the ELM method is described in Section 3. Section 4 presents the detailed experimental designs. The experimental results and discussion of the proposed method are presented in Section 5. Finally, conclusions and recommendations for future work are summarized in Section 6.
Section snippets
Extreme learning machine (ELM)
A brief description of ELM is given in this section, for more details, one can refer to [9], [17]. Given a training set , where is the input feature vector and is a target vector. The standard SLFNs with an activation function and hidden neurons can be mathematically modeled as follows [9]:where is the weight vector between the ith neuron in the hidden layer and the input layer, means the bias of the ith
Proposed ELM model
In this section, we briefly described the proposed ELM method for distinguishing PQ-poisoned patients from the healthy controls. As depicted in Fig. 1, the diagnosis model is created by ELM. From the figure, the input data consist of the weights of metabolites in blood. The optimal hidden nodes in the hidden layer of ELM will be determined by the ten repetitions of 3-fold cross-validation method. When the trained model was performed for prediction, the output of ELM has two statuses, ‘1’ means
Data description
This study was approved by the Medical Ethics Committee of The First Affiliated Hospital of Wenzhou Medical University and conducted in accordance with the Declaration of Helsinki. All individual information of PQ-poisoned patients was securely protected and only available to the investigators. All data were analyzed anonymously.
There were 15 patients (aged from 18–64 years, 9 male/6 female) who had a history of direct contact with PQ poisoning were involved in this study. Their timeframe of PQ
Experimental results and discussions
Different types of activation functions may have different impact on the performance of ELM. Therefore, the first attempt was made to evaluate the influence of different activation functions on the performance of the ELM model. Five different types of activation function including Sigmoid function (sig), Sine function (sin), Hard-limit function (hardlim), Triangular basis function (tribas) and Radial basis function (radbas) were evaluated in the experiment. The relationship between the
Conclusions and future work
This paper presents a novel method for diagnosis of PQ poisoning from the machine learning perspective. The empirical experiments on the data, collected from The First Affiliated Hospital of Wenzhou Medical University, have demonstrated the excellent superiority of the proposed ELM based approach in terms of classification accuracy, AUC, sensitivity and specificity. With the aid of feature selection, we have identified the most crucial feature for PQ poisoning diagnosis. Based on these
Acknowledgments
This research is supported by the National Natural Science Foundation of China (NSFC) (61303113, 81401558 and 61402337). This work is also supported by Science and Technology Committee of Shanghai Municipality of China (KF1405), Zhejiang Provincial Natural Science Foundation of China (LY14H230001, LQ13G010007, LQ13F020011, LY14F020035), and the key construction academic subject (medical innovation) of Zhejiang Province (11-CX26).
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Contributed equally to this work.