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Arxiv
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Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations
- Researchers propose an interpretable Machine Learning (ML) framework for Multidrug Resistance (MDR) prediction.
- The framework models patients as Multivariate Time Series (MTS) and uses various similarity measures to quantify patient interactions.
- It achieves an AUC of 81% and outperforms baseline ML and deep learning models in MDR prediction.
- The approach identifies key risk factors and reveals clinically relevant clusters, supporting early detection and patient stratification.
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