Natural Language Processing and Automated Speech Recognition to Identify Older Adults with Cognitive Impairment

自然语言处理和自动语音识别可识别患有认知障碍的老年人

• 批准号: 10383696
• 负责人: Alex D Federman
• 金额: $ 81.03万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10383696
• 关键词: Acoustics; Acute; Address; Algorithms; Alzheimer' s disease related dementia; Caregivers; Chicago; Clinical; Clinical assessments; Code; Cognition; Cognitive; Data; Data Analyses; Data Element; Data Scientist; Data Set; Development; Diagnosis; Diagnostic; Documentation; Early Diagnosis; Elderly; Electronic Health Record; Health Services; Health system; Impaired cognition; Individual; Insurance Carriers; Machine Learning; Measures; Mental disorders; Methods; Natural Language Processing; Neurocognitive; New York City; Parkinson Disease; Patient Care; Patients; Persons; Physicians; Population; Positioning Attribute; Preventive care; Primary Health Care; Procedures; Provider; Psychiatric Diagnosis; Reference Standards; Research; Research Personnel; Resource Allocation; Risk Factors; Sampling; Semantics; Sensitivity and Specificity; Services; Signs and Symptoms; Speech; Structure; Study Subject; Technology; Testing; Text; Time; Training; United States; Validation; Visit; adverse event risk; aging population; automated speech recognition; base; care coordination; clinical encounter; cognitive function; cognitive testing; deep learning; demographics; diagnostic algorithm; electronic data; electronic structure; falls; feature extraction; financial incentive; health care settings; improved; insurance claims; machine learning algorithm; machine learning classifier; mental state; mild cognitive impairment; multidisciplinary; prevent; primary care setting; recruit; risk mitigation; screening; secondary analysis; structured data; success; testing services; tool; treatment choice; unstructured data

Project Summary The purpose of this proposal is to develop two strategies, natural language processing (NLP) and automated speech analysis (ASA), to enable automated identification of patients with cognitive impairment (CI), from mild cognitive impairment (MCI) to Alzheimer’s Disease Related Dementias (ADRD) in clinical settings. The number of older adults in the United States with MCI and ADRD is increasing and yet the ability of clinicians and researchers to identify them at scale has advanced little over recent decades and screening with clinical assessments is done inconsistently. Alternative strategies using available data, like analysis of diagnostic codes in the clinical record or insurance claims, have very low sensitivity. NLP and ASA used with machine learning are technologies that could greatly increase ability to detect MCI and ADRD in clinical contexts. NLP automatically converts text in the electronic health record (EHR) into structured concepts suitable for analysis. Thus, clinicians’ documentation of signs and symptoms or orders of tests and services that reflect or address cognitive limitations can be efficiently captured, possibly long before the clinician uses an ADRD-related diagnostic code. ASA directly measures cognition by recognizing different features of cognition captured in speech. Extracting features through both NLP and ASA could thus provide a unique measure of cognition and its impact on the individual and their caregivers. Early detection of MCI and ADRD can help researchers identify appropriate patients for research and help clinicians and health systems target patients for preventive care and care coordination. For these reasons, more efficient, highly scalable strategies are needed to identify people with MCI and ADRD. The Specific Aims of this proposal are to (1) Develop and validate a ML algorithm using features extracted from the EHR with NLP to identify patients with CI, (2) Develop and validate a ML algorithm using features extracted from ASA of audio recordings of patient-provider encounters during routine primary care visits to identify patients with CI, (3) Develop and validate a ML algorithm using both NLP and ASA extracted features to create an integrated CI diagnostic algorithm. We will develop machine learning algorithms using NLP and ASA extracted features trained against neurocognitive assessment data on 800 primary care patients in New York City and validate them in an independent sample of 200 patients in Chicago. In secondary analyses we will train ML algorithms to identify MCI and its subtypes. This project will be the most rigorous development of NLP, ASA, and ML algorithms for CI yet performed, the first to test ASA in primary care settings, and the first to test NLP and ASA feature extraction strategies in combination. The multi-disciplinary team of clinicians, health services researchers, and neurocognitive and data scientists will apply machine learning to develop these highly scalable, automated technologies for identification of MCI and ADRD. 1

项目摘要 该提案的目的是制定两种策略：自然语言处理（NLP）并自动化 语音分析（ASA），以实现对认知障碍患者（CI）的自动鉴定， 在临床环境中，对阿尔茨海默氏病有关的痴呆症（ADRD）的认知障碍（MCI）。数字 在美国，有MCI和ADRD的老年人正在增加，但临床医生和 近几十年来，研究人员以大规模识别它们几乎没有进步，并通过临床进行筛查 评估不一致。使用可用数据的替代策略，例如诊断分析 临床记录或保险索赔中的代码具有非常低的灵敏度。 NLP和ASA与机器一起使用 学习是可以大大提高在临床环境中检测MCI和ADRD的能力的技术。 NLP 自动将电子健康记录（EHR）中的文本转换为适合分析的结构化概念。 这是临床医生的标志，符号或测试和服务的订单的文档，以反映或解决 可以有效地捕获认知局限 诊断代码。 ASA通过识别捕获的认知的不同特征直接测量认知 演讲。因此，通过NLP和ASA提取特征可以提供对认知和 它对个人及其护理人员的影响。 早期发现MCI和ADRD可以帮助研究人员确定适当的患者进行研究和帮助 临床医生和卫生系统针对预防性护理和护理协调的患者。由于这些原因， 需要更有效，高度可扩展的策略来识别MCI和ADRD的人。具体目标 该建议的内容是（1）使用从EHR中提取的特征来开发和验证ML算法 NLP识别CI患者，（2）使用从ASA的ASA提取的特征开发和验证ML算法 常规初级保健访问期间患者提供者遭遇的音频记录以识别CI患者， （3）使用NLP和ASA提取的特征开发和验证ML算法以创建集成的CI 诊断算法。我们将使用NLP和ASA提取的功能开发机器学习算法 针对纽约市800名初级保健患者的神经认知评估数据培训并验证 他们在芝加哥的200名患者的独立样本中。在次要分析中，我们将训练ML算法 识别MCI及其亚型。该项目将是NLP，ASA和ML最严格的开发 CI的算法，第一个在初级保健设置中测试ASA的算法，以及第一个测试NLP和ASA 结合特征提取策略。临床医生，卫生服务的多学科团队 研究人员以及神经认知和数据科学家将应用机器学习来发展这些 可扩展的自动化技术，用于识别MCI和ADRD。 1