Title: On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit Show Chinese title

Title: 时间序列核和降维在重症监护室中识别抗菌多药耐药性获得的应用

Abstract: The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients admitted to the Intensive Care Units (ICU) is a major global concern. This study analyses data in the form of multivariate time series (MTS) from 3476 patients recorded at the ICU of University Hospital of Fuenlabrada (Madrid) from 2004 to 2020. 18\% of the patients acquired AMR during their stay in the ICU. The goal of this paper is an early prediction of the development of AMR. Towards that end, we leverage the time-series cluster kernel (TCK) to learn similarities between MTS. To evaluate the effectiveness of TCK as a kernel, we applied several dimensionality reduction techniques for visualization and classification tasks. The experimental results show that TCK allows identifying a group of patients that acquire the AMR during the first 48 hours of their ICU stay, and it also provides good classification capabilities. Show Chinese abstract

Abstract: 患者在重症监护病房（ICU）入院时获得抗菌多药耐药性（AMR）是一个重大的全球问题。 本研究分析了来自3476名患者的数据，这些数据以多变量时间序列（MTS）的形式记录于2004年至2020年间马德里Fuenlabrada大学医院的ICU中。 18%的患者在其ICU住院期间获得了AMR。 本文的目标是早期预测AMR的发展。 为此，我们利用时间序列聚类核（TCK）来学习MTS之间的相似性。 为了评估TCK作为核的有效性，我们应用了几种降维技术进行可视化和分类任务。 实验结果表明，TCK能够识别出在ICU住院最初48小时内获得AMR的患者群体，并且还提供了良好的分类能力。