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况的多模态记录，包含静态数据和多元时间序列（MTS）特征。尽管MTS是临床预测的重要工具，但将其与其他模态数据融合可能产生更深入的分析与更精确的结果。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，为促进DNN模型在临床场景中的广泛应用，亟需在可解释性方面取得根本性改进。本研究提出了一套基于可解释多模态数据驱动模型的方法，能够预测并理解西班牙马德里富恩拉夫拉达大学医院重症监护病房（ICU）中抗菌药物多药耐药性（AMR）微生物的出现。患者特征及初始健康状况通过静态变量建模，而ICU住院期间健康状况的动态演变则通过多个MTS（包括机械通气参数与抗生素摄入量）进行建模。本文提出的多模态DNN模型不仅具备高效的AMR预测能力，还融合了可解释性原理，为ICU中的AMR提供了可解释的预测支持系统。此外，我们基于多模态模型与可解释框架的方法可推广至其他涉及EHR数据的临床问题，从而拓展研究成果的影响范围与应用价值。