Title: Bayesian inference of disease co-morbidity networks from electronic health records
Speaker: Guillermo Romero Moreno
Co-morbidity networks (a.k.a. Phenotypic disease networks) capture associations between morbidities that can be later used to explore disease progression, find typical co-morbidity patterns, etc. However, current methods for building co-morbidity networks suffer known biases and their statistical properties are not well defined or exploited. Here, we provide a new method for building comorbidity networks that correct some of the previous biases by disentangling the effects of co-morbidity and the ‘natural’ mechanisms for a morbidity to appear independently. This is achieved via a Bayesian approach that infers the latent target quantities from the data while also providing full statistical distributions of the inferred quantities and other network metrics. The Bayesian approach provides further benefits since i) it is based on a flexible and interpretable model that can be understood by domain experts; ii) it allows to inject domain knowledge in the models and priors; iii) it can generate alternative realisations of the original data, which can be particularly useful for comparing sub-populations at the level of raw co-occurrence of diseases. We apply this methodology to a primary care dataset and explore the benefits and uses of this methodology, while also comparing it to previous methods for building co-morbidity networks, such as relative risk or φ-correlations. We show that our methodology provides a more robust quantification of risk and that our results are more amenable to statistical comparison.