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Hybrid AI system for delirium prediction

In hospital departments for surgical and internal medicine, acute but reversible cognitive impairment, so-called delirium, occurs in up to 50% of patients. Delirium puts nursing staff under enormous pressure. Treating patients with delirium is time-consuming and costly. However, delirium can be prevented if it is recognised in time and appropriate preventive measures are taken.

The aim of the KIDELIR project is to work closely with nursing professionals to develop hybrid AI models for delirium prediction which will support reflective nursing decisions.

  • Significant burden on nurses The care of patients with delirium, acute but reversible cognitive impairment which occurs in almost 50% of patients, is time-consuming and costly.
  • Delirium prediction model A hybrid AI model is being developed in close cooperation with nursing professionals which will enable delirium to be recognised before it occurs.
  • Relief for nursing staff The hybrid AI model for delirium prediction will support nursing professionals in making reflective decisions and taking appropriate preventative measures.

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Your key contact

  •  Prof. Dr. Christophe Kunze
    Prof. Dr. Christophe Kunze Professor for Assistive Health Technologies
    Board member - Care and Technology Lab (IMTT)
    Deputy Head - Institute for Applied Research (IAF)