Antimicrobial Resistance represents a significant challenge in the Intensive Care Unit (ICU), where patients are at heightened risk of Multidrug-Resistant (MDR) infections-pathogens resistant to multiple antimicrobial agents. This study introduces a novel methodology that integrates Gated Recurrent Units (GRUs) with advanced intrinsic and post-hoc interpretability techniques for detecting the onset of MDR in patients across time. Within interpretability methods, we propose Explainable Artificial Intelligence (XAI) approaches to handle irregular Multivariate Time Series (MTS), introducing Irregular Time Shapley Additive Explanations (IT-SHAP), a modification of Shapley Additive Explanations designed for irregular MTS with Recurrent Neural Networks focused on temporal outputs. Our methodology aims to identify specific risk factors associated with MDR in ICU patients. GRU with Hadamard's attention demonstrated high initial specificity and increasing sensitivity over time, correlating with increased nosocomial infection risks during prolonged ICU stays. XAI analysis, enhanced by Hadamard attention and IT-SHAP, identified critical factors such as previous non-resistant cultures, specific antibiotic usage patterns, and hospital environment dynamics. These insights suggest that early detection of at-risk patients can inform interventions such as preventive isolation and customized treatments, significantly improving clinical outcomes. The proposed GRU model for temporal classification achieved an average Receiver Operating Characteristic Area Under the Curve of 78.27 +- 1.26 over time, indicating strong predictive performance. In summary, this study highlights the clinical utility of our methodology, which combines predictive accuracy with interpretability, thereby facilitating more effective healthcare interventions by professionals.

翻译：抗菌药物耐药性是重症监护病房（ICU）面临的一项重大挑战，ICU患者面临多重耐药（MDR）感染——即对多种抗菌药物产生耐药性的病原体——的风险更高。本研究提出了一种新颖的方法，该方法将门控循环单元（GRUs）与先进的内在及事后可解释性技术相结合，用于检测患者随时间推移出现的MDR。在可解释性方法中，我们提出了处理不规则多元时间序列（MTS）的可解释人工智能（XAI）方法，引入了不规则时间沙普利加性解释（IT-SHAP），这是对沙普利加性解释的一种改进，专为处理具有时间输出聚焦的循环神经网络的不规则MTS而设计。我们的方法旨在识别与ICU患者MDR相关的特定风险因素。结合哈达玛德注意力的GRU模型显示出较高的初始特异性，并且其敏感性随时间推移而增加，这与长期ICU住院期间院内感染风险增加相关。通过哈达玛德注意力和IT-SHAP增强的XAI分析，识别出关键因素，例如先前的非耐药性培养结果、特定的抗生素使用模式以及医院环境动态。这些见解表明，对高危患者的早期检测可以为干预措施（如预防性隔离和定制化治疗）提供依据，从而显著改善临床结果。所提出的用于时序分类的GRU模型随时间推移实现了平均78.27 ± 1.26的受试者工作特征曲线下面积，表明其具有强大的预测性能。总之，本研究强调了我们的方法结合了预测准确性和可解释性，从而有助于医疗专业人员实施更有效的医疗干预措施的临床实用性。