  Chronic respiratory diseases, such as chronic obstructive pulmonary disease
and asthma, are a serious health crisis, affecting a large number of people
globally and inflicting major costs on the economy. Current methods for
assessing the progression of respiratory symptoms are either subjective and
inaccurate, or complex and cumbersome, and do not incorporate environmental
factors. Lacking predictive assessments and early intervention, unexpected
exacerbations can lead to hospitalizations and high medical costs. This work
presents a multi-modal solution for predicting the exacerbation risks of
respiratory diseases, such as COPD, based on a novel spatio-temporal machine
learning architecture for real-time and accurate respiratory events detection,
and tracking of local environmental and meteorological data and trends. The
proposed new machine learning architecture blends key attributes of both
convolutional and recurrent neural networks, allowing extraction of both
spatial and temporal features encoded in respiratory sounds, thereby leading to
accurate classification and tracking of symptoms. Combined with the data from
environmental and meteorological sensors, and a predictive model based on
retrospective medical studies, this solution can assess and provide early
warnings of respiratory disease exacerbations. This research will improve the
quality of patients' lives through early medical intervention, thereby reducing
hospitalization rates and medical costs.
