Tlachac M L, Rundensteiner Elke, Barton Kerri, Troppy T Scott, Beaulac Kirthana, Doron Shira
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5880-5883. doi: 10.1109/EMBC44109.2020.9176277.
Antibiotic resistant bacterial infections are a growing global health crisis. Antibiograms, aggregate antimicrobial resistance reports, are critical for tracking antibiotic susceptibility and prescribing antibiotics. This research leverages fifteen years of the expansive Massachusetts statewide antibiogram dataset curated by the Massachusetts Department of Public Health. Given the lengthy annual antibiogram creation process, data are not timely. Our prior research involved forecasting the current antimicrobial susceptibility given historic antibiograms. The objective for this research is to expand upon this prior work by identifying which antibiotic-bacteria combinations have resistance trends that are not well forecasted. For that, our proposed Previous Year Anomalous Trend Identification (PYATI) strategy employs a cluster driven outlier detection solution to identify the trends to remove before forecasting. Employing PYATI to remove antibiotic-bacteria combinations with anomalous trends statistically significantly reduces the forecasting error for the remaining combinations. As antibiotic resistance is furthered by prescribing ineffective antibiotics, PYATI can be leveraged to improve antibiotic prescribing.
抗生素耐药性细菌感染是一个日益严重的全球健康危机。抗菌谱，即抗菌药物耐药性综合报告，对于追踪抗生素敏感性和开具抗生素处方至关重要。本研究利用了马萨诸塞州公共卫生部整理的长达十五年的庞大全州抗菌谱数据集。鉴于年度抗菌谱创建过程冗长，数据不及时。我们之前的研究涉及根据历史抗菌谱预测当前的抗菌药物敏感性。本研究的目的是通过识别哪些抗生素-细菌组合的耐药趋势没有得到很好的预测，来扩展之前的工作。为此，我们提出的上一年异常趋势识别（PYATI）策略采用了一种聚类驱动的异常值检测解决方案，以识别预测前要去除的趋势。采用PYATI去除具有异常趋势的抗生素-细菌组合，可在统计学上显著降低其余组合的预测误差。由于开具无效抗生素会加剧抗生素耐药性，因此可以利用PYATI来改善抗生素处方。
