Subject Harmonization of Digital Biomarkers: Improved Detection of Mild Cognitive Impairment from Language Markers
Subject Harmonization of Digital Biomarkers: Improved Detection of Mild Cognitive Impairment from Language Markers
复制标题
数字生物标记的主题协调：改进语言标记对轻度认知障碍的检测
DOI：
--
复制
发表时间：
2023
影响因子：
--
通讯作者：
Jiayu Zhou
中科院分区：
文献类型：
--
作者：
Bao Hoang;Yijiang Pang;Hiroko H. Dodge;Jiayu Zhou
轻度认知障碍（MCI）代表包括阿尔茨海默病（AD）在内的痴呆的早期阶段，并且是治疗性干预和治疗的关键阶段。MCI的早期检测为早期干预提供了机会，并显著有益于临床试验的队列富集。血浆和脑脊液生物标志物中的成像和体内标记物具有很高的检测性能，但其高昂的成本和侵入性需要更实惠和更容易获得的替代品。数字生物标志物，特别是语言标志物的最新进展显示出巨大的潜力，其中MCI的变量信息来自语言和/或语音，随后用于预测建模。语言标记建模的一个主要挑战来自于每个人说话方式的可变性。由于大量的数据收集工作，语言研究的队列规模通常很小，人与人之间的差异使得语言标记很难推广到看不见的主题。在本文中，我们提出了一种新的主题协调工具，以解决跨学科的语言标记的分布差异的问题，从而提高机器学习模型的泛化性能。我们的实证结果表明，基于我们的协调特征构建的机器学习模型提高了对未知数据的预测性能。源代码和实验脚本可以在https://github.com/illidanlab/subject_harmonization上获得。
Mild cognitive impairment (MCI) represents the early stage of dementia including Alzheimer’s disease (AD) and is a crucial stage for therapeutic interventions and treatment. Early detection of MCI offers opportunities for early intervention and significantly benefits cohort enrichment for clinical trials. Imaging and in vivo markers in plasma and cerebrospinal fluid biomarkers have high detection performance, yet their prohibitive costs and intrusiveness demand more affordable and accessible alternatives. The recent advances in digital biomarkers, especially language markers, have shown great potential, where variables informative to MCI are derived from linguistic and/or speech and later used for predictive modeling. A major challenge in modeling language markers comes from the variability of how each person speaks. As the cohort size for language studies is usually small due to extensive data collection efforts, the variability among persons makes language markers hard to generalize to unseen subjects. In this paper, we propose a novel subject harmonization tool to address the issue of distributional differences in language markers across subjects, thus enhancing the generalization performance of machine learning models. Our empirical results show that machine learning models built on our harmonized features have improved prediction performance on unseen data. The source code and experiment scripts are available at https://github.com/illidanlab/subject_harmonization.
DOI：
10.3233/jad-171048
发表时间：
2018
期刊：
Journal of Alzheimer's disease : JAD
影响因子：
--
作者：
Wang Q;Guo L;Thompson PM;Jack CR;Dodge H;Zhan L;Zhou J;Alzheimer’s Disease Neuroimaging Initiative and National Alzheimer’s Coordinating Center
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative and National Alzheimer’s Coordinating Center