The Department of Medical Informatics is looking for two PhD students to work on cutting-edge health data science topics. This research will be performed in close collaboration with the
Observational Health Data Sciences and Informatics (OHDSI) initiative, which is a global, multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics, and the EU-sponsored
European Health Data and Evidence Network (EHDEN) and
eTransafe projects, which develop frameworks to generate reliable real-world evidence.
The PhD student will conduct research on the use of Real World Data (RWD) to improve the treatment and management of patients with chronic conditions such as asthma/COPD. This research covers all aspects of treatment care starting from research on drug utilization, treatment patterns and treatment adherence, to real world safety and effectiveness research. Also, research on risk prediction, in particular prediction of disease onset, disease progression and related outcomes will be conducted. PhD students will be responsible - under guidance of their supervisors - to generate the research question and to conduct the studies from design, analysis and interpretation of the data. For this analysis, advanced machine-learning methods will be used in close collaboration with the Health Data Science team. This research will be conducted on a wealth of international health databases mapped to the OMOP Common Data Model allowing for rapid, high quality, and replicable research.