基于多变量时间序列分析与可解释患者相似性表示的耐药性早期检测
Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations
AI总结：
本研究提出可解释的机器学习框架，利用多变量时间序列分析患者相似性，通过图网络和聚类实现多重耐药早期预测，AUC达81%，优于基线模型。
摘要：
背景与目标：多重耐药（MDR）是一个关键的全球健康问题，导致住院时间延长、医疗成本增加和死亡率上升。本研究提出一个可解释的机器学习（ML）框架用于MDR预测，旨在实现准确推断和增强可解释性。方法：患者被建模为多变量时间序列（MTS），捕捉临床进展和患者间相互作用。患者间的相似性通过基于MTS的方法量化：描述性统计、动态时间规整和时间聚类核。这些相似性度量作为MDR分类的输入，使用逻辑回归、随机森林和支持向量机，通过降维和核变换提高模型性能。为了可解释性，从这些度量构建患者相似性网络。应用谱聚类和t-SNE来识别MDR相关亚组并可视化高风险聚类，从而洞察临床相关模式。结果：该框架在Fuenlabrada大学医院的ICU电子健康记录上验证，实现了81%的AUC。它通过利用基于图的患者相似性优于基线机器学习和深度学习模型。该方法识别关键风险因素——长期使用抗生素、侵入性操作、合并感染和延长ICU住院时间——并揭示临床有意义的聚类。代码和结果可在\https://github.com/oscarescuderoarnanz/DM4MTS获取。结论：患者相似性表示结合基于图的分析提供了准确的MDR预测和可解释的见解。该方法支持早期检测、风险因素识别和患者分层，突显了可解释ML在重症监护中的潜力。
英文摘要：
Background and Objectives: Multidrug Resistance (MDR) is a critical global health issue, causing increased hospital stays, healthcare costs, and mortality. This study proposes an interpretable Machine Learning (ML) framework for MDR prediction, aiming for both accurate inference and enhanced explainability. Methods: Patients are modeled as Multivariate Time Series (MTS), capturing clinical progression and patient-to-patient interactions. Similarity among patients is quantified using MTS-based methods: descriptive statistics, Dynamic Time Warping, and Time Cluster Kernel. These similarity measures serve as inputs for MDR classification via Logistic Regression, Random Forest, and Support Vector Machines, with dimensionality reduction and kernel transformations improving model performance. For explainability, patient similarity networks are constructed from these metrics. Spectral clustering and t-SNE are applied to identify MDR-related subgroups and visualize high-risk clusters, enabling insight into clinically relevant patterns. Results: The framework was validated on ICU Electronic Health Records from the University Hospital of Fuenlabrada, achieving an AUC of 81%. It outperforms baseline ML and deep learning models by leveraging graph-based patient similarity. The approach identifies key risk factors -- prolonged antibiotic use, invasive procedures, co-infections, and extended ICU stays -- and reveals clinically meaningful clusters. Code and results are available at \https://github.com/oscarescuderoarnanz/DM4MTS. Conclusions: Patient similarity representations combined with graph-based analysis provide accurate MDR prediction and interpretable insights. This method supports early detection, risk factor identification, and patient stratification, highlighting the potential of explainable ML in critical care.
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