Sumin Park

Photoplethysmography (PPG)-based blood pressure (BP) estimation remains challenging due to inherent signal variability and the indirect nature of the measurement. However, although BP prediction is a high-stakes domain where accuracy is crucial, existing studies often overlook multimodal data that is commonly available in real-world healthcare scenarios, thereby limiting estimation accuracy. To address this gap, we introduce the Signal-driven and Demographic-Clinical Attribute dataset for BP Estimation (SABP), which integrates diverse modalities, including physiological signals, structured clinical attributes, and disease-related medical text, to comprehensively capture causally linked factors related to BP. The multimodal nature of SABP facilitates a deeper understanding of complex interactions between BP and various clinical attributes across different data modalities. However, modeling these interactions can be challenging due to unobserved variables or hidden confounders that potentially bias the underlying interaction of BP and PPG waveforms. To mitigate this issue, we propose the Causal-aware Blood Pressure Estimator (CBPE). CBPE leverages a Variational Autoencoder (VAE) to infer latent representations of hidden confounders, thereby improving the accuracy and robustness of BP estimation by effectively reducing hidden confounding effects. To complement model evaluation, we introduce the Group Loss Consistency (GLC) metric, which jointly assesses predictive performance and fairness across different BP groups. Unlike traditional error metrics, GLC explicitly penalizes performance disparities, encouraging reliable estimation across patient categories. Experimental results show that CBPE achieves competitive BP prediction accuracy while demonstrating superior robustness to subgroup bias and performance degradation under challenging conditions. Reproducible materials are available at https://github.com/MLAI-Yonsei/CBPE