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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos 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

项目摘要

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.

项目概要阿尔茨海默病 (AD) 和相关痴呆 (AD/ADRD) 的患病率预计将增加近三倍令人震惊的是，有 1300 万美国人受到影响，总护理费用预计将增加五倍，达到 1.1 到 2050 年，将耗资数万亿美元。早期发现 AD/ADRD 的前兆阶段变得极其重要，因为在现有治疗的情况下，它可以更早地为潜在的 AD/ADRD 患者引入治疗或干预初级保健往往无法诊断出早期认知能力下降的患者。医生 (PCP) 一种可以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) 中获取信息并促进 PCP 及时做出诊断将非常重要理想的，因为这将导致在我们的 I 期等效工作中对潜在的 AD/ADRD 患者进行早期干预。在哈佛医学院，我们开发了一种深度学习模型，可以及早发现认知能力下降使用麻省总医院电子病历中的临床记录，我们提出了一项直接进入第二阶段的研究，该研究进一步推进。开发新颖的深度学习算法，用于早期检测认知能力下降，并将其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，在目标 1 中，我们将开发。利用记录识别早期认知衰退患者的新颖本体论、NLP 和分类方法在目标 2 中，我们将与一线医生合作，从 EHR 中提取相关证据。设计、开发和评估以用户为中心的临床决策支持工具，以识别和管理患有以下疾病的患者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）并提示个性化一旦系统开发并经过充分测试，我们将实施及时护理的建议。在麻省总医院布里格姆医疗系统的模拟 EHR 环境中开发 CDS 工具，使用真实的该项目将通过招募初级保健主管来正式评估其实用性和可用性。不仅提供早期检测认知能力下降的有效模型，而且提供实用且经过验证的 CDS 该工具可以改善 AD/ADRD 前兆阶段的诊断，从而促进潜在潜在症状的早期干预如果成功，这将是第一个让初级保健医生和真实患者参与的研究。数据来验证这种认知衰退检测工具的实用性。