Overcoming Data Loss in Wearable Disease Detection with GAN-Based Imputation.
prospective_cohort · Level II
Where this comes from
- Record sourced from PubMed, PMID 41896594.
- Also identified by DOI 10.1038/s41746-026-02518-4 and PMC identifier 13043753.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
High rates of missing data in wearable sensor streams hinder early detection of infectious diseases, especially in low-resource settings with inconsistent device adherence and connectivity. We developed a lightweight generative adversarial network (GAN) framework that imputes missing heart rate data and integrates with a rule-based anomaly detection algorithm to identify early signs of infection. In a cohort from rural Kenya (n = 300, 161 malaria-positives), our system triggered early alerts in 100 cases, including 42 solely with imputation. Alerts preceded symptom onset by 11.9 days, aligning with the 11.7-day parasitemia window from controlled trials. Despite 50% data coverage, alerts occurred on 3.5 consecutive days during the infection window, improving early detection by 35%. The GAN, trained only on external COVID-19 data (n = 3318), generalized to malaria, reducing reconstruction error by 58%. This approach demonstrates scalable, cross-pathogen physiological monitoring and offers a robust tool for disease surveillance in settings challenged by high wearable data loss.