用原始肺功能曲线+变压器模型，提升慢阻肺诊断准确率
Transformer-based Time-Series Biomarker Discovery for COPD Diagnosis
- 用Transformer处理原始呼吸波形和人口统计信息
- 比现有方法更准且计算效率更高
- 可解释性好，结果符合医学常识
慢性阻塞性肺疾病（COPD）是一种不可逆且进行性的遗传性疾病。临床上通常使用肺功能检查的汇总指标定义COPD，但这些指标并不总足够。本文表明，使用高维原始呼吸波形能提供比汇总指标更丰富的信号。我们设计了一种基于Transformer的深度学习方法，结合原始呼吸波形与人口统计信息，预测与COPD相关的临床终点。该方法在性能上优于先前工作，同时更具计算效率。通过分析模型权重，我们提升了框架的可解释性，识别出对预测重要的呼吸波形区域。与资深肺科医生合作，进一步提供了关于呼吸波形各部分的临床见解，表明模型生成的解释与现有医学知识一致。
原文摘要 · Abstract (English)
Chronic Obstructive Pulmonary Disorder (COPD) is an irreversible and progressive disease which is highly heritable. Clinically, COPD is defined using the summary measures derived from a spirometry test but these are not always adequate. Here we show that using the high-dimensional raw spirogram can provide a richer signal compared to just using the summary measures. We design a transformer-based deep learning technique to process the raw spirogram values along with demographic information and predict clinically-relevant endpoints related to COPD. Our method is able to perform better than prior works while being more computationally efficient. Using the weights learned by the model, we make the framework more interpretable by identifying parts of the spirogram that are important for the model predictions. Pairing up with a board-certified pulmonologist, we also provide clinical insights into the different aspects of the spirogram and show that the explanations obtained from the model align with underlying medical knowledge.
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