Master Project (Winter Term 2020/2021)
In cooperation with the Surgical Department of the Charité, Universitätsmedizin Berlin.
Unforeseen events make up for a substantial part in both our personal as well as our professional lives. The easiest way to deal with those problems is to address them after they occurred. However, nowadays we have access to large databases and tools, such as machine learning, to make use of data from the past to predict and address issues before they arise in the future.
In industry and transport, preventive maintenance is already used to avoid major outage of production pipelines, or to prevent critical infrastructure breakdowns, such as for bridges and aeroplanes. In medicine, although we have access to relational and time-series databases accurately describing the patients’ history and state, this preventive approach is not widely used yet. Complications in the course of medical procedures may worsen patients’ morbidity or even putting their lives at risk, which motivates a more proactive strategy. Early detection of deteriorating patient condition is crucial to provide the best care possible and allow a quick recovery.
This is what you will address in this Master’s project, by example of complications after pancreatic surgery.