Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance.

Journal: IEEE transactions on bio-medical engineering
Published Date: Jul 23, 2025

Abstract

OBJECTIVE: Many healthcare problems involve complex patient trajectories represented as Multivariate Time Series (MTS), with predictions often coming as Time Series (TS) outputs. Despite recent advances, these "MTS-to-TS" inference tasks remain challenging due to data irregularity, temporal dependencies, and the need for clinical explainability. To address these demands, we propose novel eXplainable Artificial Intelligence (XAI) methods for "MTS-to-TS" architectures, enabling tracking of patient evolution and identification of key variable patterns associated with adverse outcomes. We evaluate our approach on private ICU data from the University Hospital of Fuenlabrada (UHF) for Multidrug Resistance (MDR) prediction and the public HiRID dataset (circulatory failure).

Authors

  • Óscar Escudero-Arnanz
    Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28942 Fuenlabrada, Spain.
  • Cristina Soguero-Ruiz
    Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain. Electronic address: cristina.soguero@urjc.es.
  • Joaquín Álvarez-Rodríguez
    University Hospital of Fuenlabrada, 28943 Fuenlabrada, Spain.
  • Antonio G Marques

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