Machine Learning Models for Predicting Unforeseen Hospital Admissions or Discharges After Anesthesia

Machine Learning Models for Predicting Unforeseen Hospital Admissions or Discharges After Anesthesia

Unexpected hospital admissions after ambulatory surgery not only bring discomfort to patients but also causes a decrease in the efficiency of the healthcare system. In addition, unanticipated patient's orientation carry the risk of unsuitable post operative orders. The hypothesis of this project is that artificial intelligence models will outperform traditional models in predicting which patients will require hospital admission after ambulatory surgery or unforeseen hospital discharge after surgery.

Inclusion Criteria:

• Patient undergoing anesthesia for a therapeutic or diagnostic procedure

Exclusion Criteria:

• Incomplete informatic data
• Error in the encoding system