Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. In the United States, more than 15.7 million Americans have been diagnosed with COPD, with 96% of individuals living with at least one other chronic health condition. It is the 4th leading cause of death in the country. Over 2.2 million patients are admitted to hospitals annually due to COPD exacerbations. Monitoring and predicting patient exacerbations on-time could save their life. This paper presents two different predictive models to predict COPD exacerbation using AI and natural language processing (NLP) approaches. These models use respiration summary notes, symptoms, and vital signs. To train and test these models, data records containing physiologic signals and vital signs time series were used. These records were captured from patient monitors and comprehensive clinical data obtained from hospital medical information systems for tens of thousands of Intensive Care Unit (ICU) patients. We achieved an area under the Receiver operating characteristic (ROC) curve of 0.82 in detection and prediction of COPD exacerbation.

翻译：慢性阻塞性肺疾病（COPD）是一种导致肺部气流受阻的慢性炎症性肺病。在美国，已有超过1570万人被诊断为COPD，其中96%的患者至少同时患有一种其他慢性健康问题。该疾病已成为美国第四大死亡原因。每年因COPD急性加重而住院的患者超过220万人。及时监测并预测患者的急性加重发作可能挽救其生命。本文提出了两种不同的预测模型，利用人工智能与自然语言处理（NLP）方法预测COPD急性加重。这些模型使用呼吸摘要记录、症状描述及生命体征数据。为训练和测试这些模型，我们采用了包含生理信号与生命体征时间序列的数据记录。这些记录采集自患者监护仪，并结合从医院医疗信息系统获取的数万名重症监护病房（ICU）患者的综合临床数据。我们在COPD急性加重的检测与预测中取得了受试者工作特征（ROC）曲线下面积0.82的结果。