An antibiogram is a periodic summary of antibiotic resistance results of organisms from infected patients to selected antimicrobial drugs. Antibiograms help clinicians to understand regional resistance rates and select appropriate antibiotics in prescriptions. In practice, significant combinations of antibiotic resistance may appear in different antibiograms, forming antibiogram patterns. Such patterns may imply the prevalence of some infectious diseases in certain regions. Thus it is of crucial importance to monitor antibiotic resistance trends and track the spread of multi-drug resistant organisms. In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future. Despite its importance, tackling this problem encounters a series of challenges and has not yet been explored in the literature. First of all, antibiogram patterns are not i.i.d as they may have strong relations with each other due to genomic similarities of the underlying organisms. Second, antibiogram patterns are often temporally dependent on the ones that are previously detected. Furthermore, the spread of antibiotic resistance can be significantly influenced by nearby or similar regions. To address the above challenges, we propose a novel Spatial-Temporal Antibiogram Pattern Prediction framework, STAPP, that can effectively leverage the pattern correlations and exploit the temporal and spatial information. We conduct extensive experiments on a real-world dataset with antibiogram reports of patients from 1999 to 2012 for 203 cities in the United States. The experimental results show the superiority of STAPP against several competitive baselines.

翻译：抗菌谱是感染患者微生物对选定抗菌药物耐药性结果的定期总结。抗菌谱有助于临床医生了解区域耐药率并在处方中选择合适的抗生素。实践中，不同抗菌谱中可能出现显著的抗生素耐药性组合，形成抗菌谱模式。此类模式可能暗示某些传染病在特定地区的流行。因此，监测抗生素耐药性趋势并追踪多重耐药微生物的传播至关重要。本文提出抗菌谱模式预测这一新问题，旨在预测未来将出现哪些模式。尽管其重要性显著，但解决此问题面临一系列挑战，且文献中尚未涉及。首先，抗菌谱模式并非独立同分布，因其潜在微生物的基因组相似性而可能彼此间存在强关联。其次，抗菌谱模式在时间上通常依赖于先前检测到的模式。此外，抗生素耐药性的传播可能受邻近或相似区域的显著影响。为应对上述挑战，我们提出一种新型时空抗菌谱模式预测框架STAPP，该框架能有效利用模式相关性并挖掘时间与空间信息。我们在包含1999年至2012年美国203个城市患者抗菌谱报告的真实数据集上进行了广泛实验。实验结果表明，STAPP相较于多个竞争基线方法具有优越性。