Scherrer Alexander, Schwidde Ilka, Dinges Andreas, Rüdiger Patrick, Kümmel Sherko, Küfer Karl-Heinz
Department of Optimization, Fraunhofer Institute for Industrial Mathematics (ITWM), Fraunhofer-Platz 1, 67663, Kaiserslautern, Germany,
Health Care Manag Sci. 2015 Sep;18(3):389-405. doi: 10.1007/s10729-014-9302-2. Epub 2014 Oct 15.
Breast cancer is the most common carcinosis with the largest number of mortalities in women. Its therapy comprises a wide spectrum of different treatment modalities a breast oncologist decides about for the individual patient case. These decisions happen according to medical guide lines, current scientific publications and experiences acquired in former cases. Clinical decision making therefore involves the time-consuming search for possible therapy options and their thorough testing for applicability to the current patient case.This research work addresses breast cancer therapy planning as a multi-criteria sequential decision making problem. The approach is based on a data model for patient cases with therapy descriptions and a mathematical notion for therapeutic relevance of medical information. This formulation allows for a novel decision support concept, which targets at eliminating observed weaknesses in clinical routine of breast cancer therapy planning.
乳腺癌是女性中最常见且致死人数最多的癌症。其治疗包括多种不同的治疗方式,由乳腺肿瘤学家针对每个患者的具体情况做出决定。这些决定是根据医学指南、当前的科学出版物以及以往病例积累的经验做出的。因此,临床决策需要耗时寻找可能的治疗方案,并对其适用于当前患者情况进行全面测试。本研究工作将乳腺癌治疗计划视为一个多标准顺序决策问题。该方法基于一个包含治疗描述的患者病例数据模型以及医学信息治疗相关性的数学概念。这种表述允许一种新颖的决策支持概念,旨在消除乳腺癌治疗计划临床常规中观察到的弱点。