School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu Province, China.
Department of Clinical Laboratory, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi 214023, Jiangsu Province, China.
Comput Intell Neurosci. 2020 May 14;2020:8841002. doi: 10.1155/2020/8841002. eCollection 2020.
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.
大数据时代，智能医疗诊断已经较为常见，虽然该技术在哮喘领域的应用还比较有限。通过常规血液生物标志物来识别哮喘患者，可以使临床诊断更容易实施，并通过数据挖掘技术来增强对关键哮喘变量的研究。我们使用健康个体的常规血液数据构建马氏距离空间（MS）。然后，我们计算了 355 名哮喘患者和 1480 名健康个体的训练常规血液数据的马氏距离，以确保 MS 的效率。正交数组和信噪比用于优化血液生物标志物变量。接收者操作特征（ROC）曲线用于确定阈值。最终，我们根据阈值在 182 名个体上验证了该系统。在 35 名哮喘患者中，MTS 正确分类了 94.15%的患者。此外，147 名健康个体中有 97.20%被正确分类。该系统分离出 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值在哮喘诊断中表现良好。总之，MTS 有望成为一种基于 7 个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，从而有可能简化诊断的复杂性并优化临床效率。
