@InProceedings{healthcare_decision_making_2025,
author="Gr{\"u}ger, Joscha
and Kuhn, Martin
and Amri, Karim
and Bergmann, Ralph",
editor="Delgado, Andrea
and Slaats, Tijs",
title="Research Paper: Enhancing Healthcare Decision-Making with Analogy-Based Reasoning",
booktitle="Process Mining Workshops",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="447--459",
abstract="Analogy-based reasoning is often employed in the treatment of hospitalized patients, especially when clinical guidelines or robust evidence bases are unavailable. This approach is based on the assumption that similar patients respond similarly to comparable treatments. Traditionally, this reasoning has relied on the memory and experience of physicians. However, the complexity of managing patient data---such as treatment sequences and responses---presents significant challenges without technological support. In particular, the procedural perspective of comparing patients is especially demanding. To address these challenges, we introduce the MAPI framework, an innovative approach for analogy-based, process-oriented search within patient data. This framework systematically manages treatment data, defines precise similarity measures, and retrieves comparable patient cases using case-based reasoning (CBR). By integrating analogy-based reasoning, MAPI enhances decision-making and improves the explainability of treatment choices, offering a more reliable and transparent tool for clinical practice.",
isbn="978-3-031-82225-4"
}

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