Predict Septic Shock in ICU Patients
Description
Reduce ICU mortality and morbidity related to septic shock and respiratory failure. Provide nurses and physicians with the opportunity to intervene earlier in the course of these high mortality, high morbidity conditions.
Detailed example
Primary outputs are risk measurements of how likely an ICU patient will develop septic shock or respiratory failure within the next 48 hours.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Improved patient outcomes through reductions in mortality and morbidity. Costs savings related to lower length of stay associated with these conditions.
Controls / human review
ATO: Not reported; PIA: Not published