基于多变量时间序列的抗菌药物多药耐药性早期预测多模态可解释数据驱动模型
Multimodal Interpretable Data-Driven Models for Early Prediction of Antimicrobial Multidrug Resistance Using Multivariate Time-Series
AI总结：
本研究提出融合静态数据与多变量时间序列的可解释多模态DNN模型，用于ICU中抗菌药物多药耐药性早期预测，兼具预测效果与可解释性，方法可推广至其他EHR相关临床问题。
摘要：
电子健康记录（EHR）是患者健康状态的固有多模态登记数据，其特征包含静态数据与多变量时间序列（MTS）。尽管MTS是临床预测的 valuable 工具，但将其与其他数据模态融合有望带来更全面的洞见与更准确的结果。深度神经网络（DNN）已成为医疗健康领域识别与界定潜在模式的基础工具。然而，DNN模型需在可解释性方面取得根本性提升，才能在临床环境中得到广泛应用。本研究提出了一种基于一组可解释多模态数据驱动模型的方法，可用于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室（ICU）中抗菌药物多药耐药（AMR）病菌的出现情况。研究利用静态变量对患者的基本情况与初始健康状态建模，同时通过机械通气、抗生素摄入等多组MTS对患者ICU住院期间的健康状态演变建模。本文提出的多模态DNN模型不仅能有效预测AMR，还融入了可解释性原则，可为ICU场景下的AMR预测提供可解释的支持系统。此外，我们提出的基于多模态模型与可解释性方案的方法体系可应用于其他涉及EHR数据的临床问题，拓展了本研究结果的影响范围与适用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
