School of Medical Technology, Hebei Medical University, Shijiazhuang, 050017, People's Republic of China.
Department of Otorhinolaryngology, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, 050011, People's Republic of China.
Sci Rep. 2023 Dec 21;13(1):22899. doi: 10.1038/s41598-023-49925-1.
Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed with bacterial or viral infections were meticulously analyzed in this study. By integrating detailed hematological insights with several machine learning algorithms, including Random Forest, Neural Networks, Decision Trees, Support Vector Machine, Naive Bayes, and Lasso Regression, for potential biomarkers were identified, with an emphasis being placed on the diagnostic significance of the Monocyte-to-Lymphocyte Ratio. Distinct inflammatory signatures associated with bacterial infections were spotlighted in this study. An innovation introduced in this research was the adaptation of the high-accuracy Lasso Regression model for the TI-84 calculator, with an AUC (95% CI) of 0.94 (0.925-0.955) being achieved. Using this adaptation, pivotal laboratory parameters can be input on-the-spot and infection probabilities can be computed subsequently. This methodology embodies an improvement in diagnostics, facilitating more effective distinction between bacterial and viral infections while fostering judicious antibiotic use.
准确区分细菌性和病毒性咽炎被认为是实现个体化治疗和合理使用抗生素的关键。本研究对 693 例咽炎患者中的 197 例明确诊断为细菌或病毒感染的患者进行了详细的数据分析。通过整合详细的血液学见解和几种机器学习算法，包括随机森林、神经网络、决策树、支持向量机、朴素贝叶斯和套索回归，确定了潜在的生物标志物，重点关注单核细胞与淋巴细胞比值的诊断意义。本研究强调了与细菌感染相关的独特炎症特征。本研究的一个创新之处是将高精度的套索回归模型应用于 TI-84 计算器，其 AUC（95%CI）为 0.94（0.925-0.955）。通过这种应用，可以现场输入关键的实验室参数，并随后计算感染概率。这种方法体现了诊断方法的改进，有助于更有效地区分细菌和病毒感染，并促进合理使用抗生素。
