van Gils Mark, Koikkalainen Juha, Mattila Jussi, Herukka Sannakaisa, Lotjonen Jyrki, Soininen Hilkka
VTT Technical Research Centre of Finland, Tampere, Finland.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2886-9. doi: 10.1109/IEMBS.2010.5626311.
Objective and early detection of Alzheimer's disease (AD) is a demanding problem requiring consideration of manymodal observations. Potentially, many features could be used to discern between people without AD and those at different stages of the disease. Such features include results from cognitive and memory tests, imaging (MRI, PET) results, cerebral spine fluid data, blood markers etc. However, in order to define an efficient and limited set of features that can be employed in classifiers requires mining of data from many patient cases. In this study we used two databases, ADNI and Kuopio LMCI, to investigate the relative importance of features and their combinations. Optimal feature combinations are to be used in a Clinical Decision Support System that is to be used in clinical AD diagnosis practice.
阿尔茨海默病（AD）的客观早期检测是一个具有挑战性的问题，需要考虑多种模态的观察结果。潜在地，许多特征可用于区分未患AD的人和处于疾病不同阶段的人。这些特征包括认知和记忆测试结果、成像（MRI、PET）结果、脑脊液数据、血液标志物等。然而，为了定义一组可用于分类器的高效且有限的特征集，需要从许多患者病例中挖掘数据。在本研究中我们使用了两个数据库，即ADNI和库奥皮奥轻度认知障碍倡议（Kuopio LMCI），来研究特征及其组合的相对重要性。最佳特征组合将用于一个临床决策支持系统，该系统将用于临床AD诊断实践。
