Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers

使用机器学习从语言和行为标记识别轻度认知障碍

基本信息

批准号：
10212669
负责人：
HIROKO Hayama DODGE
金额：
$ 228.64万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-15 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10212669
关键词：
Acoustics Address Affect Alzheimer disease screening Alzheimer&apos s Disease Alzheimer&apos s disease patient American Amyloid beta-Protein Behavior Behavior Therapy Behavior monitoring Biological Markers Cause of Death Cells Clinical Clinical Trials Cognition Cognitive Computers Data Data Set Detection Disease Progression Early Intervention Early identification Electronic Health Record Failure Health Sciences Heart Diseases Home Image Individual Internet Intervention Studies Interview Joints Language Language Development Linguistics Link Machine Learning Malignant Neoplasms Measurement Measures Medical History Memory Loss Modality Modeling Monitor Natural Language Processing Neuropsychological Tests Oregon Outpatients Participant Pathologic Patient Self-Report Patients Pattern Performance Protocols documentation Randomized Risk Scientist Signal Transduction Structure Test Result Time United States Universities Video Recording aging and technology base brain cell cognitive change cohort cost effective deep learning deep reinforcement learning demographics digital effective therapy improved in vivo information framework learning algorithm machine learning algorithm machine learning method mild cognitive impairment multimodality novel predictive modeling profiles in patients screening sensor success tau Proteins user-friendly walking speed

项目摘要

Project Summary Recent estimates indicate that Alzheimer’s disease (AD) may rank as the third leading cause of death for older people in the United States, just behind heart disease and cancer. While scientists know that AD involves a progressive brain cell failure, the reason why cells fail is still not clear. To understand the progression of the disease, one of the keys is to investigate the cognitive changes in patients with mild cognitive impairment (MCI). Even though biomarkers such as imaging and clinical functions are found to be outstanding in differentiating AD patients from those with normal cognition (NC), studies suggest that their discriminative power in early-stage MCI are rather limited. Detecting signals which distinguish subjects with MCI from those with NC is challenging due to the low sensitivity and high variability of current clinical measures such as annually assessed neuropsychological test results and self-reported functional measurements. Moreover, even though in-vivo biomarkers such as beta-amyloid and tau can be used as indicators of pathological progression towards AD, the screening of biomarkers are prohibitively expensive to be widely used among pre-symptomatic individuals in the outpatient setting. We hypothesize that progressive cognitive impact from MCI has elicited detectable changes in the way people talk and behave, which can be sensed by inexpensive and accessible sensors and leveraged by machine learning (ML) algorithms to build predictive models for quantifying the risk of MCI. Our preliminary results on a small cohort indicated that there are significant differences between MCI and NC subjects during a semi-structured conversation, and ML algorithms can use such differences for differentiating MCI and NC with promising performance. Our preliminary results in behavior monitoring also suggest highly predictive performance using temporal patterns of behavior signals. In this project, we plan to build upon our initial success and conduct comprehensive studies on language and behavior markers in larger-scale cohorts to build high-performance and interpretable ML models for screening MCI. Our three Specific Aims are: (1) Discover language markers and develop predictive models characterizing MCI. Using interview recordings from the I-CONECT project, we will use natural language processing and ML algorithms to extract linguistic and acoustic markers and develop multi- modal learning algorithms to fuse the two types of information. (2) Discover behavior markers and develop predictive models characterizing MCI. Using the in-home monitoring data from ORCATECH, we will extract short-term and long-term behavior patterns and integrate multi-granularity behavior markers to differentiate MCI and NC. (3) Linking language and behavior markers with an information framework. We will use demographics and common clinical information to profile the patients and match the two cohorts via certain similarity metrics, creating complementary features for improved prediction.

项目概要最近的估计表明，阿尔茨海默病（AD）可能成为第三大死因对于美国老年人来说，仅次于心脏病和癌症。 AD涉及进行性脑细胞衰竭，目前尚不清楚细胞衰竭的原因。疾病的进展，关键之一是调查轻度患者的认知变化尽管发现了影像学和临床功能等生物标志物。研究表明，在区分 AD 患者与认知正常 (NC) 患者方面表现出色它们在早期 MCI 检测信号中的区分能力相当有限。由于敏感性低且变异性高，MCI 受试者与 NC 受试者相比具有挑战性。当前的临床措施，例如每年评估的神经心理学测试结果和自我报告此外，尽管 β-淀粉样蛋白和 tau 蛋白等体内生物标志物可以进行功能测量。作为AD病理进展的指标，生物标志物的筛选是在门诊环境中的症状前个体中广泛使用的费用过高。我们发现，MCI 对认知的渐进影响已经引起了可察觉的变化人们的说话和行为可以通过廉价且易于使用的传感器来感知并利用通过机器学习 (ML) 算法构建预测模型来量化 MCI 的风险。一个小队列的初步结果表明，MCI 和 MCI 之间存在显着差异。半结构化对话期间的 NC 主题，ML 算法可以利用这种差异我们在行为监控方面的初步结果使 MCI 和 NC 区分开来。还建议使用行为信号的时间模式来实现高度预测性能。我们计划在初步成功的基础上对语言和行为进行全面的研究大规模队列中的标记物，以构建高性能且可解释的 ML 模型进行筛选 MCI。我们的三个具体目标是：(1) 发现语言标记并开发预测模型我们将使用 I-CONECT 项目的采访录音来表征 MCI。语言处理和机器学习算法，用于提取语言和声学标记并开发多种 (2) 发现行为标记和使用 ORCATECH 的家庭监测数据开发表征 MCI 的预测模型，我们将提取短期和长期行为模式并整合多粒度行为 (3) 将语言和行为标记与信息联系起来我们将使用人口统计数据和常见临床信息来描述患者并进行匹配。两个队列通过某些相似性指标进行比较，创建互补的特征以改进预测。