IEEE J Biomed Health Inform. 2018 Jan;22(1):276-284. doi: 10.1109/JBHI.2017.2713988. Epub 2017 Jun 8.
Forced spirometry testing is gradually becoming available across different healthcare tiers including primary care. It has been demonstrated in earlier work that commercially available spirometers are not fully able to assure the quality of individual spirometry manoeuvres. Thus, a need to expand the availability of high-quality spirometry assessment beyond specialist pulmonary centres has arisen. In this paper, we propose a method to select and optimise a classifier using supervised learning techniques by learning from previously classified forced spirometry tests from a group of experts. Such a method is able to take into account the shape of the curve as an expert would during visual inspection. We evaluated the final classifier on a dataset put aside for evaluation yielding an area under the receiver operating characteristic curve of 0.88 and specificities of 0.91 and 0.86 for sensitivities of 0.60 and 0.82. Furthermore, other specificities and sensitivities along the receiver operating characteristic curve were close to the level of the experts when compared against each-other, and better than an earlier rules-based method assessed on the same dataset. We foresee key benefits in raising diagnostic quality, saving time, reducing cost, and also improving remote care and monitoring services for patients with chronic respiratory diseases in the future if a clinical decision support system with the encapsulated classifier is to be integrated into the work-flow of forced spirometry testing.
强制肺活量测试逐渐在包括初级保健在内的不同医疗层级中普及。早期的研究表明，商业上可用的肺活量计不能完全保证个体肺活量操作的质量。因此，需要在专业肺部中心之外扩大高质量肺活量评估的可用性。在本文中，我们提出了一种使用监督学习技术选择和优化分类器的方法，通过从一组专家先前分类的强制肺活量测试中进行学习。这种方法能够考虑到专家在视觉检查期间考虑到曲线的形状。我们在专门留出用于评估的数据集上评估了最终的分类器，其接收器工作特征曲线下的面积为 0.88，特异性为 0.91 和 0.86，灵敏度为 0.60 和 0.82。此外，与其他特异性和敏感性相比，与每个专家相比，在接收器工作特征曲线上的其他特异性和敏感性水平也接近专家水平，并且优于在同一数据集上评估的早期基于规则的方法。如果将封装的分类器与临床决策支持系统集成到强制肺活量测试工作流程中，我们预计将来在提高诊断质量、节省时间、降低成本以及改善慢性呼吸系统疾病患者的远程护理和监测服务方面将具有重要意义。
