Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4325-4329. doi: 10.1109/EMBC48229.2022.9871611.
Prior work has shown the classification of voiding dysfunctions from uroflowmeter data using machine learning. We present the use of smartwatch audio, collected through the UroSound platform, in order to automatically classify voiding signals as normal or abnormal, using classical machine learning techniques. We train several classification models using classical machine learning and report a maximal test accuracy of 86.16% using an ensemble method classifier. Clinical relevance- This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous audio capture devices such as smartphones and smartwatches.
先前的研究表明，通过机器学习可以对尿流动力学数据中的排尿功能障碍进行分类。我们提出了使用智能手表音频（通过 UroSound 平台收集）的方法，以便使用经典的机器学习技术自动将排尿信号分类为正常或异常。我们使用经典的机器学习训练了几个分类模型，并报告了使用集成方法分类器的最大测试准确率为 86.16%。临床相关性- 此分类任务有可能成为泌尿科远程医疗基本工具包的一部分。它在缺乏适当医疗基础设施但仍拥有无处不在的音频捕获设备（如智能手机和智能手表）的地区特别有用。
