用于重症监护室患者多重耐药性获取不规则时间序列分类的可解释人工智能技术
Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patients
AI总结：
本研究提出结合门控循环单元（GRU）与可解释人工智能方法（IT-SHAP）的新框架，用于检测ICU患者多重耐药性发生，在保持78.27%预测准确率的同时识别关键风险因素，辅助临床早期干预。
摘要：
抗微生物药物耐药性在重症监护室（ICU）中构成了重大挑战，那里的患者面临多重耐药（MDR）感染——即对多种抗微生物药物产生耐药性的病原体——的高风险。本研究引入了一种新颖的方法，将门控循环单元（GRU）与先进的内在和事后可解释性技术相结合，用于跨时间检测患者MDR的发生。在可解释性方法中，我们提出了可解释人工智能（XAI）方法来处理不规则多变量时间序列（MTS），引入了不规则时间Shapley加性解释（IT-SHAP），这是对Shapley加性解释的修改，专为专注于时间输出的循环神经网络的不规则MTS而设计。我们的方法旨在识别ICU患者中与MDR相关的特定风险因素。带有Hadamard注意力的GRU表现出较高的初始特异性，且随时间推移敏感性增加，这与ICU长期住院期间医院感染风险增加相关。由Hadamard注意力和IT-SHAP增强的XAI分析，识别了关键因素，如先前的非耐药培养、特定抗生素使用模式和医院环境动态。这些见解表明，对高危患者的早期检测可为预防性隔离和定制治疗等干预措施提供信息，从而显著改善临床结果。所提出的用于时间分类的GRU模型随时间推移的平均受试者工作特征曲线下面积为78.27 ± 1.26，表明具有强大的预测性能。总之，本研究强调了我们的方法的临床实用性，该方法将预测准确性与可解释性相结合，从而促进专业人员更有效的医疗干预。
英文摘要：
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.
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。
