Status:

COMPLETED

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

Lead Sponsor:

HUmani

Conditions:

Anesthesia Complication

Surgery-Complications

Eligibility:

All Genders

Brief Summary

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'...

Eligibility Criteria

Inclusion

  • Patient undergoing anesthesia for a therapeutic or diagnostic procedure

Exclusion

  • Incomplete informatic data
  • Error in the encoding system

Key Trial Info

Start Date :

January 1 2020

Trial Type :

OBSERVATIONAL

Allocation :

ACTUAL

End Date :

July 30 2024

Estimated Enrollment :

68683 Patients enrolled

Trial Details

Trial ID

NCT06582407

Start Date

January 1 2020

End Date

July 30 2024

Last Update

October 18 2024

Active Locations (1)

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Page 1 of 1 (1 locations)

1

Université de Mons

Mons, Belgium, 7000

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