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 identification 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 self-reported asthma cases and healthy controls from the Colive Voice study. The multimodal model achieved an AUROC of 0.83 and Brier score of 0.18, outperforming unimodal approaches. Exploratory analysis of the gating mechanism in asthma cases showed that greater respiratory symptom burden was associated with increased weighting of reading-passage modality and reduced weighting of sustained-vowel phonation modality. These findings support the feasibility of voice-based identification of self-reported asthma; however, independent prospective validation remains necessary before clinical use.