Personalized Bayesian Inference for Explainable Healthcare Management and Intervention

Chronic healthcare conditions such as Asthma requires constant monitoring and managing of symptoms and their triggers for better quality of life. Each asthma patient reacts very differently to potential triggers. Hence, there is a need to develop a explainable personalized framework for each patient to capture susceptibility to asthma triggers. We developed a personalized knowledge-based probabilistic model to predict asthma exacerbation for different environmental factors utilizing patient generated health data from pediatric asthma patients. Further, the personalized model provides a metric, called Health Coefficient, to quantify the health of a patient for varying environmental factors. We demonstrate the predictive capabilities of the developed stochastic model and discuss its viability in managing asthma symptoms and opportunity for early clinical intervention.