An Architecture Based on Fuzzy Systems for Personalized Medicine in ICUs

This paper proposes a decision support system based on fuzzy clustering, fuzzy modeling and fuzzy fingerprints, to provide personalized therapy for critically ill patients. It is hypothesized that the ‘collective experience’ from large clinical databases, where clinical decisions are linked with patient outcomes, can be used to identify specific patient sub-groups and build personalized therapy models towards a new era of personalized medicine, allowing the improvement of patient outcomes in the Intensive Care Unit (ICU). The validity of the proposed systems will be tested using the case study of patients admitted to the ICU who then develop acute kidney injury (AKI); Two-thirds of patients with AKI require renal support therapy. Generalized severity scoring systems have consistently performed poorly for patients with AKI.