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

项目摘要 阿尔茨海默病(AD)和相关痴呆症(AD/ADRD)的患病率预计将增加近两倍，达到 令人震惊的1300万受影响的美国人和总的医疗费用预计将增加五倍，达到1.1 到2050年将达到万亿美元。AD/ADRD前驱阶段的早期检测变得极其重要， 因为在现有治疗的情况下，它可以更早地对潜在的AD/ADRD患者进行治疗或干预 充其量只能带来适度的好处。初级保健往往会低估患者的早期认知能力下降 医生(PCP)。一种可自动检测认知衰退信号的临床决策支持(CDS)工具 从纵向电子健康记录(EHR)中获取并帮助初级保健医生做出及时的诊断将是非常有意义的 这是可取的，因为这将导致对潜在的AD/ADRD患者进行早期干预。在我们的第一阶段同等工作中 在哈佛医学院，我们开发了一种深度学习模型，用于更早地检测认知能力下降 在麻省总医院的电子病历中使用临床笔记。在这里，我们建议进行一项直接到第二阶段的研究，该研究进一步 开发新的深度学习算法以早期检测认知能力下降，并将其实现为 临床决策支持工具，并在初级保健环境中验证该工具。具体地说，在目标1中，我们将开发 利用病历识别早期认知功能减退患者的新本体、NLP和分类方法 从EHR中提取相关证据，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。我们打算与CMS等循证框架保持一致的系统 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 及时护理的建议。一旦该系统开发完成并经过充分测试，我们将实施 在MASS General Brigham Healthcare System的模拟EHR环境中开发了CDS工具，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实用性和可用性。这个项目将 不仅提供有效的认知衰退早期检测模型，而且提供实用和有效的CDS 可以改进AD/ADRD先兆阶段诊断的工具，从而促进对潜在的早期干预 AD/ADRD患者。如果成功，这将是第一个涉及初级保健医生和真实患者的研究 数据来验证这种认知衰退检测工具的实用性。

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.