  Electronic health records (EHR) is an inherently multimodal register of the
patient's health status characterized by static data and multivariate time
series (MTS). While MTS are a valuable tool for clinical prediction, their
fusion with other data modalities can possibly result in more thorough insights
and more accurate results. Deep neural networks (DNNs) have emerged as
fundamental tools for identifying and defining underlying patterns in the
healthcare domain. However, fundamental improvements in interpretability are
needed for DNN models to be widely used in the clinical setting. In this study,
we present an approach built on a collection of interpretable multimodal
data-driven models that may anticipate and understand the emergence of
antimicrobial multidrug resistance (AMR) germs in the intensive care unit (ICU)
of the University Hospital of Fuenlabrada (Madrid, Spain). The profile and
initial health status of the patient are modeled using static variables, while
the evolution of the patient's health status during the ICU stay is modeled
using several MTS, including mechanical ventilation and antibiotics intake. The
multimodal DNNs models proposed in this paper include interpretable principles
in addition to being effective at predicting AMR and providing an explainable
prediction support system for AMR in the ICU. Furthermore, our proposed
methodology based on multimodal models and interpretability schemes can be
leveraged in additional clinical problems dealing with EHR data, broadening the
impact and applicability of our results.
