UIOBigHealth: improving causal inference using machine learning and big data analytics on real-world health data
Contact person: Hedvig Marie Egeland Nordeng
Keywords: Computational Life Sciences, Machine learning, Big Data analytics, Pharmacoepidemiology, Causal inference, Pregnancy
Research groups: PharmaSafe, UIORealArt
Department of Pharmacy, Department of Informatics
Norway is home to some of the world's most comprehensive and well-maintained health care registries and birth cohorts. These contain valuable data on patient demographics, diagnoses, treatments, and outcomes including million of patients as well as generic/epigenetic data from the MoBa biobank. To date, 98 000 samples (trios) have been analyzed in genome-wide association studies (GWAS), and are available to us. Our projects unite researchers who together aim to move beyond conventional pharmacoepidemiological studies by combining novel, state-of-the-art computational, big data analytics with causal inference