BioMed
Machine Learning for Respiratory Health and Pediatric Asthma: A Dual Framework Combining Environmental Prediction of Respiratory Hospitalizations with Digital Biomarkers of Adherence to Diaphragmatic Breathing
By Daniel Pereira Ferreira, Gabriel Fuscald Scursone and Diana F. Adamatti.
Abstract
Background
Asthma is a chronic respiratory disease shaped by environmental, meteorological, and behavioral factors. Few approaches combine population-level surveillance with individual-level monitoring within a single analytical framework.
Methods
This work developed a dual machine learning framework. Study 1 modeled the daily count of respiratory admissions (chapter X of the ICD-10) in São Paulo, Brazil, from 2017 to 2022 as a nowcasting task, combining ElasticNet, residual CatBoost, direct CatBoost, and adaptive blending, validated by walk-forward over 30 bimonthly windows. Study 2 applied an XGBoost and Random Forest pipeline to 913 diaphragmatic-breathing sessions from 17 patients aged 9 to 16 years in the Respire Bem system, with Asthma Control Test and salivary cortisol represented using evidence-based synthetic simulation.