Automatic Generation of Risk Classification for Decision Support in Critical Care1

Modern critical care medicine is marked by a steadily growing amount of available patient data. This causes a new challenge in extracting significant information concerning the patient’s prognosis or diagnosis. Classic non-computer science approaches of decision support are prognostic scoring systems like the APACHE Scoring System. Surprisingly, these risk scores although very common in the U.S.A., are not widely used within decision processes on intensive care units (ICU) in Germany. This paper addresses this phenomenon and suggests a methodology for determining risks for a given patient, based on previous experience with other patients. The here presented automatic risk classification is based on knowledge discovery in databases (KDD).