Clinical trials are a good source for alternative therapies for cancer patients. However, mapping cancer patient profiles to clinical trials is a time consuming task, because the search parameters are generic. Matching the patient's specific tumor parameters to the clinical trials requires reading the eligibility criteria of a huge amount of clinical trials.

In this project, we work on a tool for clinicians to find and rank potentially relevant clinical trials for their cancer patients. This requires a more specialized search for fitting clinical trials by matching cancer patient profiles using methods of information extraction, clinical text processing, clustering and topic extraction, ranking of results and sentence complexity analysis. The goal of this project is to provide a time saving alternative to reduce the number of clinical trials which has to be read by clinicians by filtering and ranking clinical trials eligibility criteria for a given patient profile.