自动筛查哮喘和慢性阻塞性肺病的移动平台
2017/07/24
慢阻肺和哮喘分别占据全球疾病负担的很大比例，其中慢阻肺是全世界第三大主要死因，而哮喘是最普遍的慢性疾病，二者影响超过3亿人口。其疾病负担主要集中在发展中国家，这些国家的患者难以得到接受过肺部疾病诊断训练的医生的诊治。因此，这些患者中漏诊和误诊的比例较高。为了满足此需求，我们开发出一种可以筛查哮喘和慢阻肺的移动平台，该解决方案基于移动智能手机，包括电子听诊器、最大流量计应用程序以及患者调查问卷。利用这些数据结合机器学习算法以识别哮喘以及慢阻肺患者。为测试和验证该设计，通过我们的用户移动程序收集119名健康和患病参与者的数据，并用计算机进行分析。同时，所有个体均接受有经验的肺科医生使用完整的肺功能测试仪器检查作为对照。我们算法利用两级逻辑回归模型，首先能从一般人群中识别哮喘或慢阻肺患者，ROC曲线的 AUC为0.95。在识别出这些患者之后，该算法能够区分哮喘患者和慢阻肺患者， ROC曲线的AUC为0.97。该研究对利用独立移动手机平台在全球众多地区实现筛查和诊断肺部疾病具有重要的里程碑意义。
A mobile platform for automated screening of asthma and chronic obstructivepulmonary disease.
Chamberlain DB, Kodgule R, Fletcher RR.
Chronic Obstructive Pulmonary Disease (COPD) and asthma each represent a large proportion of the global disease burden; COPD is the third leading cause of death worldwide and asthma is one of the most prevalent chronic diseases, afflicting over 300 million people. Much of this burden is concentrated in the developing world, where patients lack access to physicians trained in the diagnosis of pulmonary disease. As a result, these patients experience high rates of underdiagnosis and misdiagnosis. To address this need, we present a mobile platform capable of screening for Asthma and COPD. Our solution is based on a mobile smart phone and consists of an electronic stethoscope, a peak flow meter application, and a patient questionnaire. This data is combined with a machine learning algorithm to identify patients with asthma and COPD. To test and validate the design, we collected data from 119 healthy and sick participants using our custom mobile application and ran the analysis on a PC computer. For comparison, all subjects were examined by an experienced pulmonologist using a full pulmonary testing laboratory. Employing a two-stage logistic regression model, our algorithms were first able to identify patients with either asthma or COPD from the general population, yielding an ROC curve with an AUC of 0.95.Then, after identifying these patients, our algorithm was able to distinguish between patients with asthma and patients with COPD, yielding an ROC curve with
AUC of 0.97. This work represents an important milestone towards creating a self-contained mobile phone-based platform that can be used for screening and diagnosis of pulmonary disease in many parts of the world.
Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:5192-5195. doi:10.1109/EMBC.2016.7591897.
(复旦大学附属中山医院呼吸科 李蕾摘译 杨冬审校)
（Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:5192-5195. doi:10.1109/EMBC.2016.7591897.）
（Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:5192-5195. doi:10.1109/EMBC.2016.7591897.）
A mobile platform for automated screening of asthma and chronic obstructivepulmonary disease.
Chamberlain DB, Kodgule R, Fletcher RR.
Chronic Obstructive Pulmonary Disease (COPD) and asthma each represent a large proportion of the global disease burden; COPD is the third leading cause of death worldwide and asthma is one of the most prevalent chronic diseases, afflicting over 300 million people. Much of this burden is concentrated in the developing world, where patients lack access to physicians trained in the diagnosis of pulmonary disease. As a result, these patients experience high rates of underdiagnosis and misdiagnosis. To address this need, we present a mobile platform capable of screening for Asthma and COPD. Our solution is based on a mobile smart phone and consists of an electronic stethoscope, a peak flow meter application, and a patient questionnaire. This data is combined with a machine learning algorithm to identify patients with asthma and COPD. To test and validate the design, we collected data from 119 healthy and sick participants using our custom mobile application and ran the analysis on a PC computer. For comparison, all subjects were examined by an experienced pulmonologist using a full pulmonary testing laboratory. Employing a two-stage logistic regression model, our algorithms were first able to identify patients with either asthma or COPD from the general population, yielding an ROC curve with an AUC of 0.95.Then, after identifying these patients, our algorithm was able to distinguish between patients with asthma and patients with COPD, yielding an ROC curve with
AUC of 0.97. This work represents an important milestone towards creating a self-contained mobile phone-based platform that can be used for screening and diagnosis of pulmonary disease in many parts of the world.
Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:5192-5195. doi:10.1109/EMBC.2016.7591897.
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美国哮喘或慢阻肺人群中确诊患有百日咳患者的经济负担：一项源于管理性申报数据库的研究
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哮喘移动健康研究——使用ResearchKit的大型临床观察性研究
