Multimodal Digital Biomarker for Asthma: Complementary Roles of Vocal, Clinical and Demographic Factors

Abstract

Asthma affects over 260 million people worldwide, yet diagnosis remains dependent on spirometry and specialist assessment, limiting accessibility in primary care and low-resource settings. Vocal biomarkers offer a promising non-invasive alternative, but prior studies have largely focused on acoustic features without integrating clinical context. We present a multimodal Mixture-of-Experts framework for asthma detection that adaptively combines acoustic embeddings from sustained vowel phonation and reading passage tasks with structured clinical and demographic data. The model was evaluated on a matched cohort of 1,218 asthma cases and healthy controls from the Colive Voice study. The multimodal model achieved an AUROC of 0.85 and Brier score of 0.17, outperforming unimodal and bimodal approaches. Adaptive gating analysis revealed increased reliance on audio features in participants with greater respiratory symptom burden, whereas clinical features contributed more strongly in less symptomatic individuals. These findings support scalable and explainable asthma screening using smartphone-collected voice recordings.