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Thursday, 5th November, 2020, 12:00

Systems medicine temporal analysis of patient disease trajectories

Incorporating the temporal dimension into disease comorbidity studies has shown to be crucial for achieving a better understanding of the disease progression. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a global view of the underlying mechanisms and thus, shed more light on their aetiology. In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. Prostate cancer is, herein, selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease associations in different disease spaces.

Speaker: Alexia Giannoula (Researcher at the Integrative Biomedical Informatics group of GRIB)

Room Zoom webinar (https://us02web.zoom.us/j/81073249721)



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