Rüping Stefan, Anguita Alberto, Bucur Anca, Cirstea Traian Cristian, Jacobs Björn, Torge Antje
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:3214-7. doi: 10.1109/EMBC.2013.6610225.
Clinical decision support (CDS) systems promise to improve the quality of clinical care by helping physicians to make better, more informed decisions efficiently. However, the design and testing of CDS systems for practical medical use is cumbersome. It has been recognized that this may easily lead to a problematic mismatch between the developers' idea of the system and requirements from clinical practice. In this paper, we will present an approach to reduce the complexity of constructing a CDS system. The approach is based on an ontological annotation of data resources, which improves standardization and the semantic processing of data. This, in turn, allows to use data mining tools to automatically create hypotheses for CDS models, which reduces the manual workload in the creation of a new model. The approach is implemented in the context of EU research project p-medicine. A proof of concept implementation on data from an existing Leukemia study is presented.
临床决策支持（CDS）系统有望通过帮助医生高效地做出更好、更明智的决策来提高临床护理质量。然而，用于实际医疗用途的CDS系统的设计和测试很繁琐。人们已经认识到，这很容易导致系统开发者的想法与临床实践需求之间出现问题性的不匹配。在本文中，我们将提出一种降低构建CDS系统复杂性的方法。该方法基于对数据资源的本体注释，这提高了数据的标准化和语义处理能力。反过来，这允许使用数据挖掘工具为CDS模型自动创建假设，从而减少创建新模型时的人工工作量。该方法是在欧盟研究项目p-医学的背景下实施的。文中展示了对来自现有白血病研究数据的概念验证实施情况。
