Signal Processing and Multimedia Communications Research Group, School of Computing and Mathematics, Plymouth University, Plymouth, PL4 8AA, UK.
IEEE Trans Biomed Eng. 2013 Jan;60(1):164-8. doi: 10.1109/TBME.2012.2212278. Epub 2012 Aug 8.
Diagnosis of Alzheimer's disease (AD) is often difficult, especially early in the disease process at the stage of mild cognitive impairment (MCI). Yet, it is at this stage that treatment is most likely to be effective, so there would be great advantages in improving the diagnosis process. We describe and test a machine learning approach for personalized and cost-effective diagnosis of AD. It uses locally weighted learning to tailor a classifier model to each patient and computes the sequence of biomarkers most informative or cost-effective to diagnose patients. Using ADNI data, we classified AD versus controls and MCI patients who progressed to AD within a year, against those who did not. The approach performed similarly to considering all data at once, while significantly reducing the number (and cost) of the biomarkers needed to achieve a confident diagnosis for each patient. Thus, it may contribute to a personalized and effective detection of AD, and may prove useful in clinical settings.
阿尔茨海默病（AD）的诊断通常较为困难，尤其是在疾病的早期阶段，即轻度认知障碍（MCI）阶段。然而，正是在这个阶段，治疗最有可能有效，因此改进诊断过程将具有巨大的优势。我们描述并测试了一种用于 AD 个性化和具有成本效益的诊断的机器学习方法。它使用局部加权学习来为每个患者定制分类器模型，并计算出最具信息量或最具成本效益的生物标志物序列来诊断患者。使用 ADNI 数据，我们对 AD 与对照组以及在一年内进展为 AD 的 MCI 患者进行了分类，而对未进展为 AD 的患者进行了分类。该方法的表现与一次考虑所有数据相似，同时大大减少了为每个患者做出明确诊断所需的生物标志物的数量（和成本）。因此，它可能有助于 AD 的个性化和有效检测，并可能在临床环境中证明有用。
