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 阿尔茨海默氏病（AD）和相关痴呆症（AD/ADRD）的患病率将几乎三倍 惊人的1300万受影响的美国人和总护理成本预计将增加五倍，达到1.1 到2050年，数万亿美元。早期发现AD/ADRD的前体阶段变得极为重要， 因为它可以对潜在的AD/ADRD患者引入治疗或干预措施，但给定现有治疗 充其量只有适中的好处。患者的早期认知能力下降通常在初级保健诊断下受到诊断 医师（PCP）。可以自动检测认知下降信号的临床决策支持（CDS）工具 从纵向电子健康记录（EHR）和促进PCP进行及时诊断将是高度的 理想的是，这将导致潜在的AD/ADRD患者的早期干预。在我们阶段我等效的工作 在哈佛医学院，我们开发了一种深度学习模型，用于早期发现认知能力下降 在弥撒将军的EHRS中使用临床笔记。在这里，我们提出了一项直接跨越II研究，进一步 开发了新颖的深度学习算法以早期检测认知能力下降，将它们实施到 临床决策支持工具，并在初级保健环境中验证该工具。具体来说，在AIM 1中，我们将发展 新的本体论，NLP和分类方法使用记录鉴定早期认知能力下降的患者 来自EHR，并从临床注释中提取相关证据。在AIM 2中，我们将与一线医生合作 设计，开发和评估以用户为中心的临床决策工具，以识别和管理患者 认知能力下降。我们打算与循证框架（例如CMS）保持一致的系统 协作护理模型，将确定有风险的患者（有证据证据），并促使个性化 建议及时护理。一旦系统开发并进行了全面测试，我们将实施 使用REAL 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。这个项目将 不仅提供有效的模型以早期检测认知能力下降，还提供了实用且经过验证的CD 可以改善AD/ADRD前体阶段诊断的工具，从而促进早期干预措施 AD/ADRD患者。如果成功，这将是第一项与初级保健医生和真正患者有关的研究 数据以验证这种认知下降检测工具的效用。