KIDELIR

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Prevention against delirium

Hybrid AI system for delirium prediction to relieve the burden on nursing practice

In surgical and internal medicine hospital wards, up to 50 percent of patients experience acute but recoverable cognitive impairment, so-called delirium. Delirium leads to enormous stress in nursing practice. The care of patients with delirium is complex and time-consuming. However, delirium can be avoided if it is recognized in good time and appropriate preventive measures are taken.

The aim of the KIDELIR project is to develop hybrid AI models for delirium prediction and to support reflective nursing decisions with the close involvement of nursing professionals.

  • 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)