Luo Andrew Z, Whitmire Eric, Stout James W, Martenson Drew, Patel Shwetak
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4239-4242. doi: 10.1109/EMBC.2017.8037792.
Spirometry plays a critical role in characterizing and improving outcomes related to chronic lung disease. However, patient error in performing the spirometry maneuver, such as from coughing or taking multiple breaths, can lead to clinically misleading results. As a result, spirometry must take place under the supervision of a trained specialist who can identify and correct patient errors. To reduce the need for specialists to coach patients during spirometry, we demonstrate the ability to automatically detect four common patient errors. Creating separate machine learning classifiers for each error based on features derived from spirometry data, we were able to successfully label errors on spirometry maneuvers with an F-score between 0.85 and 0.92. Our work is a step toward reducing the need for trained individuals to administer spirometry tests by demonstrating the ability to automatically detect specific errors and provide appropriate patient feedback. This will increase the availability of spirometry, especially in low resource and telemedicine contexts.
肺活量测定在表征和改善与慢性肺病相关的结果方面起着关键作用。然而，患者在进行肺活量测定操作时出现的错误，如咳嗽或多次呼吸，可能会导致临床上产生误导性的结果。因此，肺活量测定必须在经过培训的专家监督下进行，专家能够识别并纠正患者的错误。为了减少专家在肺活量测定过程中指导患者的需求，我们展示了自动检测四种常见患者错误的能力。基于从肺活量测定数据中提取的特征为每种错误创建单独的机器学习分类器，我们能够成功地对肺活量测定操作中的错误进行标记，F值在0.85至0.92之间。我们的工作朝着减少对经过培训的人员进行肺活量测定测试的需求迈出了一步，通过展示自动检测特定错误并提供适当患者反馈的能力。这将提高肺活量测定的可及性，尤其是在资源匮乏和远程医疗环境中。
