DeepCOPD: Development and Implementation of Deep Learning to Predict and Prevent COPD Health Care Encounters

在美国，约2400万人患有COPD，其中一半未确诊，约15万人死于COPD 每年。COPD导致超过700，000例美国住院治疗，每年花费近500亿美元。的 如果疾病进展和其他因素，COPD的人力和经济负担可能会减少， 不良事件是可以预期的，使护理人员能够将有限的资源集中用于有风险的病人。 我们建议创建一个决策支持工具，将生物医学信息学与先进的 机器学习（ML）和深度学习（DL）算法，用于预测急性和慢性医疗保健 遭遇（入院、再入院和艾德遭遇）和重大疾病进展 事件（家庭氧疗）。这样的工具将立即提供临床 并加速对COPD疾病进展和治疗的研究。预测建模是 广泛用于识别COPD和其他疾病的护理管理高危患者， 强调再入院风险。然而，现有的技术不够准确， 未识别未来可能发生的医疗事件的具体性质，未估计事件发生时间， 预测COPD患者的医疗遭遇和疾病进展事件。最近 疾病进展建模的研究支持DL和其他ML方法的应用， 电子健康记录（EHR）预测健康史的各个方面。EHR很容易包含这两种 可访问的结构化数据（例如，定义明确的字段中的实验室结果）和非结构化文本，例如 医生的笔记非结构化文本包含大量的临床信息，但这些信息是 难以接近;妨碍其在研究和临床中的常规使用。这促使人们试图 使用自然语言处理（NLP）方法来自动化注释。我们将应用NLP来识别 症状，治疗，程序，诊断，社会风险因素和临床功能状态 注释，扩展了EHR中可用的数据，远远超出了通常的编码变量。拉巴和靠近约旦河之 与此不同的是，我们将对所提出的预测工具进行逐步楔形临床实施 并评估其性能，首次用于COPD健康事件的ML和DL预测。所以我们 提出四个具体目标：目标1：转换EHR数据流，以提供患者级功能集 用于ML和DL消费。目标2：开发一组ML和DL模型来预测事件发生时间 用于COPD患者的家庭氧疗启动和医疗保健。目标3： 制定和实施预期性能监督和校准维护系统， 为每个结果保留最终的目标2模型。目标4：评估 DeepCOPD工具包在两个医疗保健系统中近实时临床使用。应用程序 响应NHLBI IDEA 2 Health（NOT-HL-19-712）。

In the US, ~24 million persons live with COPD, half undiagnosed, and ~150,000 die of COPD annually. COPD causes over 700,000 US hospitalizations and costs nearly $50 billion per year. The human and financial burdens of COPD could likely be reduced if disease progression and other adverse events could be anticipated, enabling caregivers to focus finite resources on at-risk patients. We propose to create a decision-support tool that integrates biomedical informatics with advanced machine learning (ML) and deep learning (DL) algorithms to predict acute and chronic healthcare encounters (hospital admissions, readmissions, and ED encounters) and major disease progression events (home oxygen therapy) for outpatients with COPD. Such a tool would confer immediate clinical benefits and accelerate research on COPD disease progression and treatment. Predictive modeling is widely used to identify high-risk patients for care management in COPD and other disorders, with a strong emphasis on readmission risk. However, extant techniques are not sufficiently accurate and do not identify the specific nature of likely future medical events, estimate time-to-event, and specifically forecast medical encounters and disease progression events for individuals with COPD. Recent research in disease progression modeling support the application of DL and other ML methods to electronic health records (EHRs) to predict aspects of health history. EHRs contain both readily accessible structured data (e.g., lab results in well-defined fields) and unstructured texts such as physician’s notes. Unstructured texts contain a great deal of clinical information, but this information is laborious to access; impeding its routine use in research and the clinic. This has motivated attempts to use natural language processing (NLP) methods to automate annotation. We will apply NLP to identify symptoms, treatments, procedures, diagnoses, social risk factors, and functional status from clinical notes, expanding the data available from EHRs far beyond the usual coded variables. Also, and distinctively, we will carry out a stepped-wedge clinical implementation of the proposed predictive tool and evaluate its performance, a first for ML and DL prediction of COPD health events. Therefore, we propose four Specific Aims: AIM 1: Transform EHR data streams to provision patient-level feature sets for ML and DL consumption. AIM 2: Develop a set of ML and DL models to predict the time-to-event for home oxygen therapy initiation and healthcare encounters among patients with COPD. AIM 3: To develop and implement a prospective performance surveillance and calibration maintenance system to maintain the final Aim 2 model for each outcome. AIM 4: Evaluate adoption and usability of the DeepCOPD toolkit in near-realtime clinical use in two healthcare systems. The application is responsive to the NHLBI IDEA2Health (NOT-HL-19-712).