Speaker
Description
We investigate a large set of electronic health records (EHRs) collected by wellbeing services county of Soutwest Finland (Varha) [1]. Different diseases have different prevalence in a given population. For this reason, the observation of a specific comorbidity in a given patient could be just the result of a random co-occurrence of two unrelated diseases. Therefore a comorbidity net- work of diseases obtained from EHR data can in principle mix comorbidity occurrences originating either from random or from biological/medical origin. To extract from EHR data information on biologically or medically induced comorbidity we perform the detection of so-called statistically validated networks [2,3]. In this approach, all links of a projected network (PROJ) obtained start- ing from a bipartite network (patient-disease) are subjet to a statistical test. Each link in the PROJ network of diseases is subjected to a statistical test able to discriminate whether the presence of a link is an indication of comorbidity of unknown origin (i.e. in technical terms compatible with a so-called “null hypothesis”) or as an indication of potential comorbidity of biological/medical origin. By performing the same statistical test for all diseases’ pairs present in the PROJ network we extract what we address as a SVN. The SVN is providing a selection of those comorbidities that cannot be statistically explained only by random co-occurrence of diseases of different prevalence.
[1] P. Crisafulli, T. Galla, A. Karlsson, R.N. Mantegna, S. Miccichè, and J. Piilo, Statistically Validated Comorbidity Networks, manuscript in preparation (2025). [2] M. Tumminello et al. Statistically validated networks in bipartite complex systems. PLoS ONE, 6(3):e17994, Mar. 2011. [3] M.-X. Li et al . Statistically validated mobile communication networks: the evolution of motifs in european and chinese data. New Journal of Physics, 16(8):083038, 2014.
