Clinical Decision Support System for Early Detection of Cognitive Decline Using Electronic Health Records and Deep Learning

利用电子健康记录和深度学习早期检测认知衰退的临床决策支持系统

• 批准号: 10603902
• 负责人: Jingcheng Du
• 金额: $ 112.14万
• 依托单位: MELAX TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10603902
• 关键词: Aducanumab; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Behavioral; Brain; Caring; Classification; Clinical; Clinical Decision Support Systems; Cognitive; Communication; Data; Data Set; Detection; Development; Diagnosis; Early Diagnosis; Early Intervention; Electronic Health Record; Environment; FDA approved; Healthcare; Healthcare Systems; Impaired cognition; Information Systems; Knowledge; Modeling; Neurobehavioral Manifestations; Neurologist; Ontology; Patients; Phase; Physicians; Prevalence; Primary Care Physician; Provider; Publishing; Recommendation; Research; Risk; Risk Factors; Senile Plaques; Signal Transduction; Small Business Innovation Research Grant; Specialist; Symptoms; System; Technology; Testing; Time; Validation; Work; care costs; clinical center; clinical decision support; clinical diagnosis; clinical efficacy; collaborative care; computerized; deep learning; deep learning algorithm; deep learning model; design; evidence base; experience; health care service organization; health record; improved; information model; medical schools; mild cognitive impairment; novel; phase 2 study; primary care clinician; primary care setting; prototype; recruit; research and development; social engagement; social health determinants; support tools; tool; usability

Project Summary The prevalence of Alzheimer’s disease (AD) and related dementia (AD/ADRD) is expected to nearly triple to a staggering 13 million affected Americans and the total costs of care are projected to increase five-fold to 1.1 trillion dollars by the year 2050. Early detection of precursor stages of AD/ADRD becomes extremely important, as it can introduce treatment or intervention earlier for potential AD/ADRD patients, given existing treatments only have modest benefit at best. Early cognitive decline of patients is often under diagnosed by primary care physicians (PCPs). A clinical decision support (CDS) tool that can automatically detect cognitive decline signals from longitudinal electronic health records (EHRs) and facilitate PCPs to make timely diagnoses would be highly desirable, as it would result in early intervention for potential AD/ADRD patients. In our Phase I Equivalent work at Harvard Medical School, we have developed a deep learning model for earlier detection of cognitive decline using clinical notes in Mass General Brigham’s EHRs. Here we propose a Direct-to-Phase II study, which further develops novel deep learning algorithms for the early detection of cognitive decline, implement them into a clinical decision support tool, and validate the tool in a primary care setting. Specifically, in Aim 1, we will develop novel ontology, NLP, and classification approaches to identify patients with early cognitive decline using records from EHR and extract related evidence from clinical notes. In Aim 2, we will work with frontline physicians to design, develop and evaluate a user-centered clinical decision support tool to identify and manage patients with cognitive decline. The system, which we intend to align with evidence-based frameworks such as the CMS Collaborative Care Model, will identify patients at risk (with supporting evidence) and prompt personalized recommendations for timely care. Once the system is developed and fully tested, we will implement the developed CDS tool in a simulated EHR environment at Mass General Brigham healthcare system, using real patient data, and formally evaluate its utility and usability by recruiting primary care clinicians. This project will deliver not only effective models for early detection of cognitive decline, but also a practical and validated CDS tool that can improve diagnosis of precursor stages of AD/ADRD, thus facilitating early intervention for potential AD/ADRD patients. If successful, it will be the first study that engages primary care physicians and real patient data to validate the utility of such a cognitive decline detection tool.

项目摘要 阿尔茨海默病（AD）和相关痴呆（AD/ADRD）的患病率预计将接近三倍， 令人震惊的1300万受影响的美国人和护理的总成本预计将增加五倍，达到1.1 万亿美元的资金。AD/ADRD前体阶段的早期检测变得极其重要， 因为在现有治疗的情况下，它可以为潜在的AD/ADRD患者更早地引入治疗或干预 最多只能带来有限的好处。初级保健往往对患者早期认知能力下降的诊断不足 医生（PCP）。一种临床决策支持（CDS）工具，可以自动检测认知衰退信号 从纵向电子健康记录（EHR）和促进PCP及时诊断将是高度 这是可取的，因为它将导致对潜在的AD/ADRD患者的早期干预。在我们的第一阶段等效工作中， 在哈佛医学院，我们开发了一种深度学习模型，用于早期检测认知能力下降。 用布里格姆将军电子病历里的临床记录在这里，我们提出了一个直接到第二阶段的研究，进一步 开发新的深度学习算法，用于早期检测认知衰退，将其应用于 临床决策支持工具，并在初级保健环境中验证该工具。具体而言，在目标1中，我们将开发 使用记录识别早期认知衰退患者的新型本体、NLP和分类方法 并从临床记录中提取相关证据。在目标2中，我们将与前线医生合作， 设计，开发和评估以用户为中心的临床决策支持工具，以识别和管理患者 认知能力下降我们打算将该系统与CMS等基于证据的框架保持一致 协作护理模式，将识别有风险的患者（有支持证据），并提示个性化 建议及时护理。待系统发展及全面测试后，我们会实施 在马萨诸塞州布里格姆医疗保健系统的模拟EHR环境中开发CDS工具，使用真实的 患者数据，并通过招募初级保健临床医生正式评估其效用和可用性。该项目将 不仅提供有效的模型，早期检测认知能力下降，而且还提供实用和有效的CDS 这是一种可以改善AD/ADRD前驱阶段诊断的工具，从而有助于对潜在的AD/ADRD进行早期干预。 AD/ADRD患者。如果成功的话，这将是第一个让初级保健医生和真实的病人参与的研究 数据来验证这种认知下降检测工具的效用。