Maria Medeleanu

Machine learning (ML) encompasses a group of AI methods that allow us to better identify the patterns and relationships between respiratory data (such as pulmonary function tests(PFT)) and outcomes of interest (like asthma or wheezing). Traditional statistical methods characterize such patterns with mathematical equations but in ML, computer analyses large volumes of data to learn complex, non‐linear relationships that enable greater accuracy. ML also enables the analysis of types of data that were previously not amenable to computational analysis, such as imaging and auditory data.

I am interested particularly interested in how ML could enhance the analysis of PFT scores. Complex, multidimensional patterns of PFT variation may identify disease subtypes, personalizing diagnosis and treatment. Standardization of PFT interpretation could also be improved with AI applications.