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

• 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
• 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' s Disease; Alzheimer' 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

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，阿尔茨海默病(AD)可能是导致死亡的第三大原因 对于美国的老年人来说，仅次于心脏病和癌症。虽然科学家们知道 AD涉及进行性脑细胞衰竭，细胞衰竭的原因尚不清楚。要了解 本病的进展，关键之一是要了解轻症患者的认知改变。 认知障碍(MCI)。即使发现了成像和临床功能等生物标记物 研究表明，在区分AD患者和认知正常(NC)患者方面表现突出 他们在早期MCI中的辨别能力相当有限。检测区分的信号 由于MCI的低敏感性和高变异性，来自NC的MCI的受试者具有挑战性 目前的临床措施，如每年评估神经心理测试结果和自我报告 功能测量。此外，即使体内的生物标记物，如β-淀粉样蛋白和tau可以 作为AD病理进展的指标，筛选生物标记物有 在门诊环境中广泛应用于无症状个体的成本高得令人望而却步。 我们假设，来自MCI的渐进性认知影响已经在方式上引起了可检测的变化 人们的谈话和行为，可以通过廉价和可访问的传感器来感知并利用 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 一小群人的初步结果表明，MCI和MCI之间存在显著差异 NC主题在半结构化对话期间，ML算法可以将这种差异用于 以优异的性能区分MCI和NC。我们在行为监测方面的初步结果 还建议使用行为信号的时间模式进行高度预测性的表现。在这个项目中， 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 在更大规模的队列中建立用于筛查的高性能和可解释的ML模型的标记 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 MCI的特征。使用I-CONECT项目的采访录音，我们将使用Natural 语言处理和ML算法，以提取语言和声学标记，并开发多个 融合两类信息的模式学习算法。(2)发现行为标记物 开发具有MCI特征的预测模型。使用来自ORCATECH的家庭监控数据， 我们将提取短期和长期行为模式，并整合多粒度行为 用于区分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学和常见的临床信息来描述患者和匹配 这两组人通过一定的相似性度量，创建了互补的特征，以改进预测。