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: 10709094
• 负责人: HIROKO Hayama DODGE
• 金额: $ 33.03万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10709094
• 关键词: Administrative Supplement; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; American; Amyloid beta-Protein; Behavior; Behavior monitoring; Biological Markers; Brain imaging; Cause of Death; Cells; Clinical; Clinical Markers; Clinical Trials; Cognition; Cognitive; Cohort Studies; Data; Data Sources; Databases; Dementia; Detection; Digital biomarker; Disease Progression; Early Diagnosis; Early Intervention; Early identification; Effectiveness; Elderly; Failure; Funding; Goals; Heart Diseases; Home; Image; Individual; Language; Language Development; Lead; Learning; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Memory Loss; Modality; Modeling; Neuropsychological Tests; Outpatients; Pathologic; Patient Self-Report; Patients; Pattern; Performance; Persons; Proteomics; Reporting; Residential Facilities; Risk; Sample Size; Scientist; Signal Transduction; Structure; Test Result; Training; United States; Work; brain cell; cognitive change; cohort; computer framework; cost effective; effective therapy; imaging biomarker; improved; in vivo; insight; learning algorithm; machine learning algorithm; machine learning model; mild cognitive impairment; multimodality; novel; parent project; predictive modeling; response; screening; sensor; success; tau Proteins; transfer learning; user-friendly

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 the parent project, we are building 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. This supplement builds on our current work on digital biomarkers and will focus on further refining the prediction capability of digital biomarkers. Recently, the availability of MRI data from I-CONECT study has provided Unanticipated Opportunity for us to dramatically improve the quality of digital biomarkers. To achieve this goal, in Aim S1 we propose to develop a data-driven algorithms framework that uses high-quality imaging information as auxiliary information to increase the predictive performance of language markers; in Aim S2 we propose to develop a computational framework to use public language databases to improve the quality of language markers. This supplement, if funded, will significant predictive performance improvements of digital biomarkers and therefore improve the predictive power of early detection of MCI.

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。在父母中 项目，我们正在基于最初的成功，并就语言和 大规模同伙中的行为标记，以建立高性能和可解释的ML模型 筛选MCI。这种补充是基于我们当前在数字生物标志物上的工作，并将重点放在 进一步完善数字生物标志物的预测能力。最近，从 I-Conect研究为我们提供了意想不到的机会，可以极大地提高 数字生物标志物。为了实现这一目标，在AIM S1中，我们建议开发数据驱动算法 使用高质量成像信息作为辅助信息来增加预测性的框架 语言标记的性能；在AIM S2中，我们建议开发一个计算框架以使用 公共语言数据库，以提高语言标记的质量。如果资助的话，这种补充剂将 数字生物标志物的显着预测性能改善，因此改善了 MCI早期检测的预测能力。

项目成果

Corticosteroids for infectious critical illness: A multicenter target trial emulation stratified by predicted organ dysfunction trajectory.

皮质类固醇治疗传染性危重疾病：按预测的器官功能障碍轨迹分层的多中心目标试验模拟。

• DOI: 10.1101/2024.03.07.24303926
• 发表时间: 2024
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Rajendran,Suraj;Xu,Zhenxing;Pan,Weishen;Zang,Chengxi;Siempos,Ilias;Torres,Lisa;Xu,Jie;Bian,Jiang;Schenck,EdwardJ;Wang,Fei
• 通讯作者: Wang,Fei

Development of a federated learning approach to predict acute kidney injury in adult hospitalized patients with COVID-19 in New York City.

开发联合学习方法来预测纽约市成年住院 COVID-19 患者的急性肾损伤。

• DOI: 10.1101/2021.07.25.21261105
• 发表时间: 2021
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Jaladanki,SurajK;Vaid,Akhil;Sawant,AshwinS;Xu,Jie;Shah,Kush;Dellepiane,Sergio;Paranjpe,Ishan;Chan,Lili;Kovatch,Patricia;Charney,AlexanderW;Wang,Fei;Glicksberg,BenjaminS;Singh,Karandeep;Nadkarni,GirishN
• 通讯作者: Nadkarni,GirishN

Robust Unsupervised Domain Adaptation from A Corrupted Source

• DOI: 10.1109/icdm54844.2022.00171
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Shuyang Yu;Zhuangdi Zhu;Boyang Liu;Anil K. Jain;Jiayu Zhou

Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning

• DOI: 10.1609/aaai.v37i7.25955
• 发表时间: 2021-06
• 期刊: Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
• 作者: Junyuan Hong;Haotao Wang;Zhangyang Wang;Jiayu Zhou

An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals.

• DOI: 10.1016/j.patter.2023.100898
• 发表时间: 2024-01-12
• 期刊: PATTERNS
• 影响因子: 6.5
• 作者: Pan, Weishen;Xu, Zhenxing;Rajendran, Suraj;Wang, Fei
• 通讯作者: Wang, Fei