2021/07/07 by Óscar Escudero-Arnanz, Joaquín Rodríguez-Álvarez, Escudero-Arnanz, Óscar +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Immunology and Microbiology · Medicine · #Advanced Chemical Sensor Technologies #Anomaly Detection Techniques and Applications #Antibiotic Use and Resistance #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #Metabolomics and Mass Spectrometry Studies #Pneumonia and Respiratory Infections #Populations and Evolution (q-bio.PE) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2107.10398
openalex publication_date 2021/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.