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

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

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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.

项目摘要最近的估计表明，阿尔茨海默氏病（AD）可能是死亡的第三大原因对于美国的老年人，就在心脏病和癌症之后。虽然科学家知道 AD涉及进行性脑细胞衰竭，原因仍然无法清楚。理解疾病的进展，其中之一是研究中部患者的认知变化认知障碍（MCI）。即使发现了成像和临床功能之类的生物标志物为了使AD患者与正常认知患者（NC）的出色表现出色，研究表明他们在早期MCI中的判别力量相当有限。检测有区别的信号由于敏感性低和高可变性，具有NC患者的MCI受试者是具有挑战性的当前的临床指标，例如每年评估神经心理学测试结果并自我报告功能测量。而且，即使体内生物标志物（例如β-淀粉样蛋白和tau）也可以用作病理发展向AD的指标，生物标志物的筛查是在门诊环境中广泛使用的症状较高。我们假设MCI的渐进认知影响引起了可检测的方式人们说话和行为，可以通过便宜且可访问的传感器来感知和杠杆通过机器学习（ML）算法来构建预测模型，以量化MCI的风险。我们的小队列的初步结果表明，MCI和在半结构化对话中的NC受试者，ML算法可以使用此类差异通过有希望的表现区分MCI和NC。我们在行为监控的初步结果还建议使用行为信号的临时模式提出高度预测性的性能。在这个项目中，我们计划以最初的成功为基础，并就语言和行为进行全面研究大规模同伙的标记，以建立高性能和可解释的ML模型用于筛选 MCI。我们的三个具体目的是：（1）发现语言标记并开发预测模型表征MCI。使用I-Conect项目的访谈记录，我们将使用自然语言处理和ML算法提取语言和声学标记，并发展多种多样模态学习算法融合了两种类型的信息。（2）发现行为标记和开发表征MCI的预测模型。使用来自Orcatech的家庭监视数据，我们将提取短期和长期行为模式以及综合的多粒性行为区分MCI和NC的标记。（3）将语言和行为标记与信息联系起来框架。我们将使用人口统计和常见的临床信息来介绍患者并匹配通过某些相似性指标，这两个队列，创建了完整的功能以改进预测。