A deep learning algorithm to detect signs of cognitive impairment in electronic health records

用于检测电子健康记录中认知障碍迹象的深度学习算法

基本信息

批准号：
10900991
负责人：
Sudeshna Das
金额：
$ 84.34万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
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：
2023-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10900991
关键词：
Accountability Active Learning Address Algorithms Alzheimer&apos s disease diagnosis Alzheimer&apos s disease related dementia Appointment Artificial Intelligence Behavioral Caring Characteristics Classification Clinical Code Cognitive Cohort Studies Communities Complex Computer software Computerized Medical Record Computers Data Data Element Data Set Databases Dementia Detection Diagnosis Elderly Electronic Health Record Electronics Emergency department visit Ensure Entropy Epidemiologist Evaluation Functional disorder Future Geography Guidelines Health Health Care Costs Health Professional Health Sciences Health system Healthcare Impaired cognition Individual Institution Intervention Knowledge Label Learning Measures Medical Records Methods Modeling Natural Language Processing Online Systems Outcome Outcome Study Patient Care Management Patients Pattern Performance Pharmaceutical Preparations Provider Recording of previous events Reference Standards Research Research Personnel Research Priority Resources Sample Size Sampling Scientist Site Source Specialist Specific qualifier value Structure Symptoms Technology Testing Texas Text Training Universities Validation Wisconsin Work adjudication algorithmic bias annotation system burden of illness clinical care clinical phenotype deep learning deep learning algorithm deep learning model demographics drug repurposing epidemiology study health care service utilization health care settings improved innovation learning strategy mild cognitive impairment multidisciplinary pragmatic trial research study screening source localization structured data tool unstructured data

项目摘要

Alzheimer’s Disease and Related Dementias (AD/ADRD) outcomes from real-world data, such as electronic health records (EHR), offer the possibility of examining a wide variety of research questions that cannot be answered efficiently—or at all—in other settings. A key challenge is that AD/ADRD is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data—and can be mislabeled in any setting. Thus, approaches relying on dementia diagnosis codes or medications suffer from inaccuracies in these data. EHR has a wealth of information in clinical notes, patient health history, and health system interactions that often contain signs of cognitive decline. Deep learning algorithms can leverage and learn from these complex text and data patterns in EHR. In this proposal, we aim to develop and evaluate a deep learning algorithm to improve the detection of cognitive impairment due to underlying AD/ADRD pathophysiology (including cognitive concerns, mild cognitive impairment, and dementia) using the EHR of three large healthcare institutions. For training and evaluation of the algorithm, we will use a “seed” reference standard set with detailed chart review and adjudication of cognitive diagnosis by an expert clinician (n=1,000), and then apply active learning strategies with diversity sampling to better reflect the characteristics of US older adults and iteratively increase sample size to n=20,000. We will rigorously evaluate the algorithm using EHR from all three institutions, and develop openly available guidelines and resources for the research community. Our specific aims are: 1) To develop and evaluate a deep learning NLP tool to identify patients with cognitive impairment using EHR at one institution; 2) To refine and evaluate the performance of our EHR deep learning algorithm at two other healthcare institutions; and 3) To develop open guidelines, resources, and tools for EHR data use in dementia research. We will measure the marginal improvement in accuracy of our deep learning- based classification relative to models based on diagnosis codes and medications alone, and characterize the predictors of poor model performance, both to improve the model and to understand potential biases. As such, our tool will provide a better understanding of the limitations of using diagnosis codes and/or medications in dementia research. Cutting-edge deep learning algorithms have been applied to many real-world tasks but in a limited manner to AD/ADRD. We anticipate that our state-of-the-art deep learning algorithm, which will be rigorously developed and validated with large representative datasets at multiple institutions, will more efficiently and accurately detect signs of cognitive impairment and can be readily deployed by practitioners. Improved screening of cognitive impairment in EHR will enhance dementia research studies and enable large- scale pragmatic trails. In the future, we hope, the proposed tool will also be useful in clinical settings to flag patients with cognitive impairment who could benefit from an evaluation or be referred to specialist care.

来自现实世界数据的阿尔茨海默氏病和相关痴呆症（AD/ADRD）结果，例如电子健康记录（EHR），提供了检查各种各样的研究问题的可能性在其他设置中有效地回答或根本回答。一个关键的挑战是AD/ADRD在社区，由医疗保健专业人员诊断不远，在索赔数据中编码不足 - 可以是在任何情况下都标记了标签。这是依靠痴呆诊断代码或药物遭受的方法这些数据的不准确性。 EHR在临床笔记，患者健康病史和健康方面有很多信息系统相互作用通常包含认知能力下降的迹象。深度学习算法可以利用和从EHR中的这些复杂的文本和数据模式中学习。在此提案中，我们旨在开发和评估深度学习算法以改善由于基础广告/ADRD引起的认知障碍的检测使用EHR的病理生理学（包括认知问题，轻度认知障碍和痴呆症）三个大型医疗机构。为了培训和评估算法，我们将使用“种子”参考具有详细图表审查和对认知诊断的详细图表的标准集（n = 1,000），然后应用具有多样性抽样的主动学习策略以更好地反映我们年长的特征成人和迭代的样本量增加到n = 20,000。我们将使用EHR严格评估算法从所有三个机构中，并为研究界开发公开可用的准则和资源。我们的具体目的是：1）开发和评估深度学习的NLP工具以识别认知患者在一个机构使用EHR的损害； 2）完善和评估我们的EHR深度学习的表现其他两个医疗机构的算法； 3）为EHR制定开放指南，资源和工具痴呆研究中的数据使用。我们将衡量我们深度学习准确性的边际提高 - 基于基于诊断代码和药物的模型的基于模型的分类，并表征模型性能差的预测因素，无论是改善模型还是了解潜在偏见。像这样，我们的工具将更好地了解使用诊断代码和/或药物的局限性痴呆研究。尖端的深度学习算法已应用于许多现实世界任务，但广告/ADRD的有限方式。我们预计我们最先进的深度学习算法将是在多个机构的大型代表数据集中进行严格开发和验证，将更多有效，准确地检测出认知障碍的迹象，并且可以由从业者容易部署。改善EHR认知障碍的筛查将增强痴呆症研究，并使大量研究比例尺务实的步道。我们希望将来，提出的工具在临床环境中也将很有用。认知障碍的患者可以从评估中受益或被转诊为专业护理。