Simonsen A H, Mattila J, Hejl A M, Garde E, van Gils M, Thomsen C, Lötjönen J, Soininen H, Waldemar G
Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.
Dement Geriatr Cogn Disord. 2014;37(3-4):207-13. doi: 10.1159/000354372. Epub 2013 Oct 26.
The diagnosis of Alzheimer's disease (AD) is based on an ever-increasing body of data and knowledge making it a complex task. The PredictAD tool integrates heterogeneous patient data using an interactive user interface to provide decision support. The aim of this project was to investigate the performance of the tool in distinguishing AD from non-AD dementia using a realistic clinical dataset.
We retrieved clinical data from a group of patients diagnosed with AD (n = 72), vascular dementia (VaD, n = 30), frontotemporal dementia (FTD, n = 25) or dementia with Lewy bodies (DLB, n = 14) at the Copenhagen Memory Clinic at Rigshospitalet. Three classification methods were applied to the data in order to differentiate between AD and a group of non-AD dementias. The methods were the PredictAD tool's Disease State Index (DSI), the naïve Bayesian classifier and the random forest.
The DSI performed best for this realistic dataset with an accuracy of 76.6% compared to the accuracies for the naïve Bayesian classifier and random forest of 67.4 and 66.7%, respectively. Furthermore, the DSI differentiated between the four diagnostic groups with a p value of <0.0001.
In this dataset, the DSI method used by the PredictAD tool showed a superior performance for the differentiation between patients with AD and those with other dementias. However, the methods need to be refined further in order to optimize the differential diagnosis between AD, FTD, VaD and DLB.
阿尔茨海默病（AD）的诊断基于日益增多的数据和知识，这使其成为一项复杂的任务。PredictAD工具使用交互式用户界面整合异构患者数据，以提供决策支持。本项目的目的是使用真实临床数据集研究该工具在区分AD与非AD痴呆方面的性能。
我们从哥本哈根大学医院记忆诊所确诊为AD（n = 72）、血管性痴呆（VaD，n = 30）、额颞叶痴呆（FTD，n = 25）或路易体痴呆（DLB，n = 14）的一组患者中检索临床数据。为区分AD和一组非AD痴呆，对数据应用了三种分类方法。这些方法是PredictAD工具的疾病状态指数（DSI）、朴素贝叶斯分类器和随机森林。
对于这个真实数据集，DSI表现最佳，准确率为76.6%，相比之下，朴素贝叶斯分类器和随机森林的准确率分别为67.4%和66.7%。此外，DSI在四个诊断组之间的区分具有p值<0.0001。
在该数据集中，PredictAD工具使用的DSI方法在区分AD患者和其他痴呆患者方面表现出卓越性能。然而，这些方法需要进一步完善，以优化AD、FTD、VaD和DLB之间的鉴别诊断。
