Enhanced Identification of Ocular Phenotypes and Outcomes in Electronic Health Record Data

增强电子健康记录数据中眼部表型和结果的识别

• 批准号: 10617779
• 负责人: Joshua D Stein
• 金额: $ 67.4万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617779
• 关键词: Acceleration; Adverse event; Algorithms; Area; Artificial Intelligence; Big Data; Bioinformatics; Biometry; Blindness; Categories; Chronic Disease; Classification; Clinical; Clinical Data; Clinical Research; Clinical Trials; Cluster Analysis; Code; Country; Data; Data Element; Data Set; Databases; Deterioration; Development; Diabetic Retinopathy; Diagnostic tests; Disease; Disease Progression; Electronic Health Record; Evolution; Eye; Eye diseases; Foundations; Future; Genotype; Glaucoma; Grant; Health Services Research; Healthcare; Intervention; Machine Learning; Macular degeneration; Manuals; Methodology; Methods; Mission; Nature; Ophthalmology; Outcome; Outcomes Research; Paper; Patient Recruitments; Patients; Phenotype; Planning Techniques; Positioning Attribute; Precision Medicine Initiative; Probability; Process; Registries; Research; Research Personnel; Research Project Grants; Running; Sensitivity and Specificity; Severity of illness; Sorting; Source; Stable Disease; Structure; Techniques; Testing; Text; Time; Translational Research; United States National Institutes of Health; Vision; Work; care providers; clinical decision-making; clinical trial recruitment; clinically relevant; cost; data registry; electronic health data; experience; flexibility; improved; innovation; interest; machine learning algorithm; novel; novel strategies; segregation; structured data; tool; unstructured data

ABSTRACT The transition from paper charts to electronic health records (EHRs), advances in computing power and storage capacity, and the availability and accessibility of sophisticated machine learning algorithms have revolutionized the ability for researchers to tap into Big Data and make use of it to answer all sorts of important clinical questions. However, maximizing the utility of all of this rich clinical data from EHRs and clinical registries is predicated on the ability for researchers to accurately identify which patients have specific diseases; to accurately classify conditions based on their disease severity; to ascertain which patients are improving, stable, or deteriorating; and to appropriately identify and quantify clinically relevant outcomes. Currently, nearly all researchers who work with Big Data in ophthalmology rely exclusively on administrative billing codes to identify common ocular diseases and outcomes of interest. Yet, research has shown that sole reliance on billing codes is fraught with limitations and does not take full advantage of the plethora of useful information routinely captured in structured and free-text EHR data. In this proposal we develop, rigorously test, and validate an innovative approach to permit researchers to more accurately identify and classify patients with common sight-threatening ocular diseases and capture transitions from less to more severe disease states and key outcomes of interest. Based on preliminary studies we performed, we believe our approach to enhanced ocular phenotype identification is substantially more accurate than exclusive reliance on billing codes. In Aim 1, we use this approach to EHR data to identify and categorize patients with 3 of the most common causes of irreversible vision loss—glaucoma, diabetic retinopathy, and macular degeneration. In Aim 2, we extend enhanced phenotype identification by trying to identify novel forms of these 3 conditions; we will use cluster analysis to identify groups of clinical features associated with these 3 ocular diseases that co-segregate together. We will also test whether some of these clusters are associated with better or worse clinical outcomes. In Aim 3, we apply our approach to identify key ocular outcomes in EHR data such as disease stability and progression from less to more advanced stages for each of the 3 ocular diseases of interest. By fulfilling the aims of this proposal, we will permit researchers throughout the country and the world to more accurately identify important ocular diseases and outcomes in EHR and clinical registry datasets. This will serve as a key building block to permit researchers to incorporate Big Data into machine learning and artificial intelligence applications, genotype-phenotype association studies, patient recruitment for clinical trials, and many other clinical and translational research projects.

摘要 从纸质病历到电子健康记录(EHR)的过渡，计算技术的进步 电力和存储容量，以及复杂机器的可用性和可访问性 学习算法彻底改变了研究人员利用大数据并使 用它来回答各种重要的临床问题。然而，最大化所有人的效用 来自EHR和临床登记处的丰富临床数据是基于研究人员的能力 准确识别哪些患者有特定的疾病；准确地对病情进行分类 根据他们的疾病严重程度；确定哪些患者正在改善、稳定或恶化； 并适当识别和量化与临床相关的结果。目前，几乎所有 在眼科使用大数据的研究人员完全依赖行政收费 识别常见眼病和感兴趣的结果的代码。然而，研究表明， 这种对帐单代码的单一依赖充满了局限性，并且没有充分利用 在结构化和自由文本的EHR数据中例行公事地捕获过多的有用信息。在这 我们开发、严格测试和验证创新方法的提案，以允许研究人员 更准确地识别和分类常见的危及视力的眼病患者 并捕捉从较轻的疾病状态到较严重的疾病状态和感兴趣的关键结果的转变。 根据我们进行的初步研究，我们认为我们的增强眼球的方法 表型识别比完全依赖帐单代码要准确得多。在……里面 目的1，我们使用这种方法对EHR数据进行识别和分类，其中 不可逆性视力丧失的常见原因--青光眼、糖尿病视网膜病变和黄斑 退化。在目标2中，我们通过尝试识别新的表型来扩展增强表型识别 这三种情况的形式；我们将使用聚类分析来确定临床特征组 与这三种共同隔离在一起的眼病有关。我们还将测试 其中一些集群与更好或更差的临床结果有关。在目标3中，我们应用 我们在EHR数据中识别关键眼部结果的方法，如疾病稳定性和 3种感兴趣的眼科疾病中的每一种都从较低的阶段进展到较晚期。通过 为了实现这项提议的目标，我们将允许全国和世界各地的研究人员 在电子病历和临床登记中更准确地识别重要的眼部疾病和预后 数据集。这将成为允许研究人员将大数据整合到 机器学习和人工智能应用，基因-表型关联研究， 为临床试验和许多其他临床和转化性研究项目招募患者。