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Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning.
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2020-05-14 , DOI: 10.1155/2020/8841002
Jun Zhan 1 , Wen Chen 2 , Longsheng Cheng 1 , Qiong Wang 2 , Feifei Han 2 , Yubao Cui 2
Affiliation  

Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. Using routine blood biomarkers to identify asthma patients would make clinical diagnosis easier to implement and would enhance research of key asthma variables through data mining techniques. We used routine blood data from healthy individuals to construct a Mahalanobis space (MS). Then, we calculated Mahalanobis distances of the training routine blood data from 355 asthma patients and 1,480 healthy individuals to ensure the efficiency of MS. Orthogonal arrays and signal-to-noise ratios were used to optimize blood biomarker variables. Receiver operating characteristic (ROC) curve was used to determine the threshold value. Ultimately, we validated the system on 182 individuals based on the threshold value. Out of 35 patients with asthma, MTS correctly classified 94.15% of patients. In addition, 97.20% of 147 healthy individuals were correctly classified. The system isolated 7 routine blood biomarkers. Among these biomarkers, platelet distribution width, mean platelet volume, white blood cell count, eosinophil count, and lymphocyte ratio performed well in asthma diagnosis. In brief, MTS shows promise as an accurate method to identify asthma patients based on 7 vital blood biomarker variables and threshold determined by the ROC curve, thus offering the potential to simplify diagnostic complexity and optimize clinical efficiency.

中文翻译:

基于常规血液生物标志物的哮喘的机器学习诊断。

智能医疗诊断在大数据时代已变得很普遍,尽管该技术仅在有限的情况下应用于哮喘。使用常规血液生物标记物识别哮喘患者将使临床诊断更容易实施,并通过数据挖掘技术加强对关键哮喘变量的研究。我们使用来自健康个体的常规血液数据来构建马氏距离(MS)。然后,我们计算了355名哮喘患者和1,480名健康个体的常规训练血液数据的Mahalanobis距离,以确保MS的效率。正交阵列和信噪比用于优化血液生物标志物变量。接收器工作特性(ROC)曲线用于确定阈值。最终,我们根据阈值对182个人进行了验证。在35例哮喘患者中,MTS正确分类了94.15%的患者。此外,正确分类了147名健康个体中的97.20%。该系统分离了7种常规血液生物标志物。在这些生物标志物中,血小板分布宽度,平均血小板体积,白细胞计数,嗜酸性粒细胞计数和淋巴细胞比率在哮喘诊断中表现良好。简而言之,MTS显示出作为基于7种重要血液生物标志物变量和ROC曲线确定的阈值来识别哮喘患者的准确方法的希望,从而提供了简化诊断复杂性和优化临床效率的潜力。该系统分离了7种常规血液生物标志物。在这些生物标志物中,血小板分布宽度,平均血小板体积,白细胞计数,嗜酸性粒细胞计数和淋巴细胞比率在哮喘诊断中表现良好。简而言之,MTS展示了作为基于7种重要血液生物标志物变量和ROC曲线确定的阈值来识别哮喘患者的准确方法的希望,从而提供了简化诊断复杂性和优化临床效率的潜力。该系统分离了7种常规血液生物标志物。在这些生物标志物中,血小板分布宽度,平均血小板体积,白细胞计数,嗜酸性粒细胞计数和淋巴细胞比率在哮喘诊断中表现良好。简而言之,MTS展示了作为基于7种重要血液生物标志物变量和ROC曲线确定的阈值来识别哮喘患者的准确方法的希望,从而提供了简化诊断复杂性和优化临床效率的潜力。
更新日期:2020-05-14
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