期刊
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
卷 114, 期 -, 页码 211-228出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neubiorev.2020.04.026
关键词
Mild cognitive impairment; MCI; AD; Neurodegenerative diseases: dementia; Biomarkers; Neuropsychological tests; Cognitive measures; Machine learning; Automatic classification
One of the current challenges in the field of Alzheimer's disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients' classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据