Passive Digital Markers (PDM)
PDM FOR ASTHMA RISK PREDICTION
Developing a Childhood Asthma Risk Passive Digital Marker: To develop and determine the usability, acceptability, feasibility, and preliminary efficacy of a childhood asthma Passive Digital Marker (PDM) for early disease detection. Here, a PDM refers to a ML algorithm that can be used to retrieve and synthesize pre-existing ‘Passively’ collected mother/child dyad prognostic data (i.e., medical history) at ages 0-3 years in ‘Digital’ EHR to provide an objective and quantifiable ‘Marker’ of a child’s asthma risk and phenotype at ages 6-10 years.
PDM FOR TREATMENT RESPONSE PREDICTION
Developing a passive digital marker (PDM) for childhood asthma treatment response prediction: Develop, validate, and evaluate the predictive performance of a childhood asthma Passive Digital Marker for treatment response prediction (PDM-TR), that is, a machine learning algorithm that can retrieve and synthesize pre-existing ‘passively’ collected mother-child dyad risk/prognostic data in ‘digital’ electronic health record (EHR) to provide an objective and quantifiable ‘marker’ of future treatment response.