Planning Tasks with High Complexity

When planning a therapy, different competing quality criteria like prospects of treatment success, risk of side effects and costs have to be considered. Thereby, chances and risks must be individually balanced for every patient.

Systemic Therapies

Treatment planning for complex diseases such as cancer, diabetes or cardiovascular diseases demands highly involved decisions from physicians.  The indication of therapy concepts must be thoroughly checked and curing chances and complication risks carefully balanced.  Typical circumstances of clinical routine such as major time pressure and limited access to medical literature turn error-free decisions about optimal therapy concepts into a very difficult task.

Clinical decision support systems provide physicians with major help for treatment planning. Knowledge databases allow for quick access to medical expertise, algorithms based on predicate logic check medical indications automatically and multi-criteria decision making methods support the balancing of curing chances and complication risks.

Physicians can with the assistance of such systems make quick and error-free therapy decisions for an optimal treatment of their patients.

Project Example: Senology Assistence

Breast cancer is the most common carcinosis and cause of death among women. Therapy typically puts surgery, chemotherapy, hormonotherapy and radiotherapy together in a suitable way. This project field addresses the data-based creation of patient-specific therapy proposals by means of mathematical decision support.


The physician plans breast cancer therapy according to his medical expertise based on therapy guide lines, scientific publications and clinical experience. This requires the handling of an enormous amount of data in order to design the best possible patient-specific therapy.


Planning software SenoAssist

The project senology assistance addresses these challenges by developing the planning software SenoAssist. The main project contents are:

  • conception of suitable data models for structuring the information relevant for therapy
  • modelling of clinical decision making with mathematical algorithm according to practical needs
  • development of a supporting software tools for the efficient design of best possible therapy plans
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Project Partner

Kliniken Essen-Mitte - Senology Breast Care Center