Irregular Temporal Classification of Multidrug Resistance Development in Intensive Care Unit Patients

The rising incidence of Multidrug-Resistant (MDR) infections in Intensive Care Units (ICUs) presents a critical challenge to global healthcare, leading to adverse patient outcomes and increased costs. Early identification of MDR pathogens is crucial for timely implementation of isolation protocols and prevention of cross-infections. This study introduces a novel Deep Learning (DL) framework for temporal prediction of MDR status in ICU patients, addressing the complexities of irregular classification tasks. We utilize Electronic Health Records from 3,502 anonymized ICU patient records spanning 17 years at the University Hospital of Fuenlabrada, Madrid, Spain. Five advanced DL architectures are systematically evaluated: Vanilla Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit, and Transformer, each adapted for irregular Multivariate Time Series (MTS) data. Notably, this is the first study to assess the impact of temporal window size on predictive performance and to adapt different RNN models for irregular temporal classification in MTS. Our results show that the LSTM model, when applied to a 4-day temporal window, achieves the highest predictive performance, with a mean Receiver Operating Characteristic Area Under the Curve of 79.78 ± 2.78. This study highlights the importance of early temporal windows in capturing critical indicators of MDR infections and significantly advances the state-of-the-art in MDR prediction, providing a robust foundation for more effective ICU management and potential reduction of MDR infections in healthcare settings.