On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit
This addresses the problem of early AMR detection in ICU patients, which is a major global health concern, but the approach is incremental as it applies existing kernel and dimensionality reduction methods to a specific dataset.
The study tackled early prediction of antimicrobial multidrug resistance (AMR) acquisition in ICU patients by analyzing multivariate time series data from 3476 patients, where 18% acquired AMR, and found that the time-series cluster kernel (TCK) could identify a group of patients acquiring AMR within the first 48 hours and provided good classification results.
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.