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.

翻译：电子健康记录（EHR）本质上是患者健康状态的多模态记录，包含静态数据和多变量时间序列（MTS）两种特征。虽然MTS是临床预测的重要工具，但其与其他数据模态的融合可能带来更全面的洞察和更精确的结果。深度神经网络（DNN）已成为识别和定义医疗领域潜在模式的基础工具，然而，要使DNN模型广泛应用于临床场景，其可解释性仍需根本性提升。本研究提出了一套基于可解释多模态数据驱动模型的方案，该模型能够预测并理解富恩拉夫拉达大学医院（西班牙马德里）重症监护病房（ICU）中抗菌药物多重耐药性（AMR）病原菌的产生。患者档案和初始健康状态通过静态变量建模，而ICU住院期间患者健康状态的演变则通过多种MTS（包括机械通气和抗生素摄入）建模。本文提出的多模态DNN模型不仅具备有效的AMR预测能力，还纳入了可解释性原则，为ICU中的AMR提供了可解释的预测支持系统。此外，我们提出的基于多模态模型与可解释性方案的方法论可应用于其他涉及EHR数据的临床问题，从而拓宽研究成果的影响范围与应用价值。