2022-05-02 17:13:51
#phd
Networks and Graphs Collaboratory - Embedding life and health
During your journey through life, you leave behind digital remnants in the health, the social, the educational, the legal system, and many more. This is a rich source of information for finding and visualizing patterns in life trajectories. Technically, we observe a number of streams of events in several channels, and a number of interactions with other individuals. The purpose of this research is to structure these as embeddings in vector spaces making visualizations, clustering, predictive analytics etc possible. Driving questions are in pharmacovigilance, in long covid tracking, in social interactions. Can we develop methods for detecting the impact on life of medications, of having had Covid, and can we quantify or even predict this?
Methodologically, we will develop deep networks inspired by the foundation models in Natural Language Processing like BERT and GPT-3 by graph neural networks and by variational autoencoders. Data-wise the project will rely on already harvested electronic health records from Capital Region and Zealand Region of Denmark comprising 2,4 mio subjects and data from Statistics Denmark on socioeconomic factors from the Danish population.
Supervisor: Mads Nielsen (University of Copenhagen, Computer Science). Co-supervisor: Sune Lehmann Jørgensen(Technical University of Denmark, DTU Informatics).
https://employment.ku.dk/phd/?show=156242
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