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

TL;DR

This paper explores using time series kernels and dimensionality reduction to predict antimicrobial resistance acquisition in ICU patients, demonstrating early identification and effective classification of at-risk patients based on multivariate time series data.

Contribution

It introduces the application of the time-series cluster kernel (TCK) combined with dimensionality reduction for early prediction of antimicrobial resistance in ICU patients, a novel approach in this context.

Findings

TCK effectively identifies patients acquiring AMR within 48 hours.

The method provides good classification accuracy.

Visualization of patient groups is enhanced by dimensionality reduction.

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.