@InProceedings{CBR_CDSS_2025,
author="Kuhn, Martin
and Warnecke, Yannik
and Preciado-Marquez, Daniel
and Gr{\"u}ger, Joscha
and Bley, Laura Isabell
and Storck, Michael
and Weishaupt, Carsten
and Bergmann, Ralph
and Braun, Stephan Alexander",
editor="Bichindaritz, Isabelle
and L{\'o}pez, Beatriz",
title="Clinical Decision Support for Skin Tumor Treatment: A Case-Based Reasoning Approach",
booktitle="Case-Based Reasoning Research and Development",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="407--422",
abstract="Cancer treatment planning is a complex and individualized process due to the variability of patient-specific factors, tumor characteristics, and evolving medical standards. Predicting the next step in diagnosis or therapy remains a significant challenge, due to the high variability and limited availability of structured medical data. Clinical Decision Support Systems (CDSS) offer a promising solution, with Case-Based Reasoning (CBR) standing out for its ability to provide interpretable and transparent recommendations. Unlike black-box machine learning models, CBR leverages past cases to generate predictions by analogy, aligning with the way clinicians naturally reason. In this work, we propose a CBR-based CDSS for skin cancer treatment that integrates medical taxonomies and patient-specific clinical features to predict the next treatment step. By focusing on both technical performance and real-world application in a medical setting, this study provides insights for the deployment of CBR-based systems in medical practice.",
isbn="978-3-031-96559-3"
}

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A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Kuhn"],"firstnames":["Martin"],"suffixes":[]},{"propositions":[],"lastnames":["Warnecke"],"firstnames":["Yannik"],"suffixes":[]},{"propositions":[],"lastnames":["Preciado-Marquez"],"firstnames":["Daniel"],"suffixes":[]},{"propositions":[],"lastnames":["Grüger"],"firstnames":["Joscha"],"suffixes":[]},{"propositions":[],"lastnames":["Bley"],"firstnames":["Laura","Isabell"],"suffixes":[]},{"propositions":[],"lastnames":["Storck"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Weishaupt"],"firstnames":["Carsten"],"suffixes":[]},{"propositions":[],"lastnames":["Bergmann"],"firstnames":["Ralph"],"suffixes":[]},{"propositions":[],"lastnames":["Braun"],"firstnames":["Stephan","Alexander"],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Bichindaritz"],"firstnames":["Isabelle"],"suffixes":[]},{"propositions":[],"lastnames":["López"],"firstnames":["Beatriz"],"suffixes":[]}],"title":"Clinical Decision Support for Skin Tumor Treatment: A Case-Based Reasoning Approach","booktitle":"Case-Based Reasoning Research and Development","year":"2025","publisher":"Springer Nature Switzerland","address":"Cham","pages":"407–422","abstract":"Cancer treatment planning is a complex and individualized process due to the variability of patient-specific factors, tumor characteristics, and evolving medical standards. Predicting the next step in diagnosis or therapy remains a significant challenge, due to the high variability and limited availability of structured medical data. Clinical Decision Support Systems (CDSS) offer a promising solution, with Case-Based Reasoning (CBR) standing out for its ability to provide interpretable and transparent recommendations. Unlike black-box machine learning models, CBR leverages past cases to generate predictions by analogy, aligning with the way clinicians naturally reason. In this work, we propose a CBR-based CDSS for skin cancer treatment that integrates medical taxonomies and patient-specific clinical features to predict the next treatment step. By focusing on both technical performance and real-world application in a medical setting, this study provides insights for the deployment of CBR-based systems in medical practice.","isbn":"978-3-031-96559-3","bibtex":"@InProceedings{CBR_CDSS_2025,\nauthor=\"Kuhn, Martin\nand Warnecke, Yannik\nand Preciado-Marquez, Daniel\nand Gr{\\\"u}ger, Joscha\nand Bley, Laura Isabell\nand Storck, Michael\nand Weishaupt, Carsten\nand Bergmann, Ralph\nand Braun, Stephan Alexander\",\neditor=\"Bichindaritz, Isabelle\nand L{\\'o}pez, Beatriz\",\ntitle=\"Clinical Decision Support for Skin Tumor Treatment: A Case-Based Reasoning Approach\",\nbooktitle=\"Case-Based Reasoning Research and Development\",\nyear=\"2025\",\npublisher=\"Springer Nature Switzerland\",\naddress=\"Cham\",\npages=\"407--422\",\nabstract=\"Cancer treatment planning is a complex and individualized process due to the variability of patient-specific factors, tumor characteristics, and evolving medical standards. Predicting the next step in diagnosis or therapy remains a significant challenge, due to the high variability and limited availability of structured medical data. Clinical Decision Support Systems (CDSS) offer a promising solution, with Case-Based Reasoning (CBR) standing out for its ability to provide interpretable and transparent recommendations. Unlike black-box machine learning models, CBR leverages past cases to generate predictions by analogy, aligning with the way clinicians naturally reason. In this work, we propose a CBR-based CDSS for skin cancer treatment that integrates medical taxonomies and patient-specific clinical features to predict the next treatment step. By focusing on both technical performance and real-world application in a medical setting, this study provides insights for the deployment of CBR-based systems in medical practice.\",\nisbn=\"978-3-031-96559-3\"\n}\n\n","author_short":["Kuhn, M.","Warnecke, Y.","Preciado-Marquez, D.","Grüger, J.","Bley, L. I.","Storck, M.","Weishaupt, C.","Bergmann, R.","Braun, S. A."],"editor_short":["Bichindaritz, I.","López, B."],"key":"CBR_CDSS_2025","id":"CBR_CDSS_2025","bibbaseid":"kuhn-warnecke-preciadomarquez-grger-bley-storck-weishaupt-bergmann-etal-clinicaldecisionsupportforskintumortreatmentacasebasedreasoningapproach-2025","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://web.wi2.uni-trier.de/publications/WI2Publikationen.bib","dataSources":["MSp3DzP4ToPojqkFy","CnwPa99ZchEF4SZgh"],"keywords":[],"search_terms":["clinical","decision","support","skin","tumor","treatment","case","based","reasoning","approach","kuhn","warnecke","preciado-marquez","grüger","bley","storck","weishaupt","bergmann","braun"],"title":"Clinical Decision Support for Skin Tumor Treatment: A Case-Based Reasoning Approach","year":2025}