Prof. Dr.-Ing. Bert Arnrich

Preventive Maintenance for Patients

Detecting Critical Conditions and Complications Before They Occur

Master Project (Winter Term 2020/2021)

Supervisors: Bjarne Pfitzner, Jonas Chromik

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.

Complications in Pancreatic Surgery

Within the field of abdominal surgery, pancreatic resections are particularly critical with postoperative mortality rates being as high as 10%. In fact, one out of four patients develops at least one major postoperative complication according to national and international studies. Hence, estimating and accurately stratifying a patient’s individual risk prior to surgery – e.g. based on his or her comorbidities, laboratory parameters, etc. – might provide crucial support in the joint decision-making process between patients and their health care professionals. Moreover, the application of machine learning approaches using pre-, intra-, and postoperative parameters might allow us to predict major complications even before they actually occur in the postoperative setting.

To this end, we will analyse a wide set of real-world-clinical data derived from >500 cases of pancreatic resections as kindly provided by the Surgical Department of the Charité, Universitätsmedizin Berlin. The goal of this project is not only to test different ML-models as to their prediction capabilities but also to evaluate the effect of missing data on prediction accuracy, for example by ignoring post-operative data and only using pre- and intra-operative data.

Resulting Publication

  • Perioperative Risk Assessment in Pancreatic Surgery Using Machine Learning. Pfitzner, Bjarne; Chromik, Jonas; Brabender, Rachel; Fischer, Eric; Kromer, Alexander; Winter, Axel; Moosburner, Simon; Sauer, Igor M.; Malinka, Thomas; Pratschke, Johann; Arnrich, Bert; Maurer, Max M. (2021). 2211–2214.