Abstract：
Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains. In this study, we assessed the viability of using infrared spectroscopy-based machine learning as a rapid and precise approach for detecting K. pneumoniae bacteria and determining its susceptibility to various antibiotics directly from a patient\'s urine sample. In this study, 2333 bacterial samples, including 636 K. pneumoniae were investigated using infrared micro-spectroscopy. The obtained spectra (27996spectra) were analyzed with XGBoost classifier, achieving a success rate exceeding 95 % for identifying K. pneumoniae. Moreover, this method allows for the simultaneous determination of K. pneumoniae susceptibility to various antibiotics with sensitivities ranging between 74 % and 81 % within approximately 40 min after receiving the patient\'s urine sample.
摘要：
引起尿路感染（UTI）的最普遍和有害的细菌是克雷伯菌（K。）肺炎。快速确定其抗生素敏感性可以增强患者治疗并减轻耐药菌株的传播。在这项研究中,我们评估了使用基于红外光谱的机器学习作为一种快速和精确的方法来检测肺炎克雷伯菌并直接从患者的尿液样本中确定其对各种抗生素的敏感性的可行性.在这项研究中,2333个细菌样本，包括636名肺炎克雷伯菌，采用红外显微光谱法进行了研究。用XGBoost分类器分析获得的光谱(27996光谱)，鉴定肺炎克雷伯菌的成功率超过95%。此外，该方法允许在接受患者尿样后约40分钟内同时测定肺炎克雷伯菌对各种抗生素的敏感性,敏感性在74%至81%之间.
