Transformer-based Time-Series Biomarker Discovery for COPD Diagnosis
This work addresses COPD diagnosis for clinicians by offering a more accurate and interpretable tool, though it is incremental as it applies an existing transformer architecture to a specific medical domain.
The paper tackled COPD diagnosis by using a transformer-based deep learning method to analyze raw spirogram data and demographic information, achieving better performance and computational efficiency than prior works while providing interpretable insights aligned with medical knowledge.
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.