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Analysis of Adverse Events in Anesthesia Using Artificial Intelligence

NCT05185479 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 9559

Last updated 2023-12-12

No results posted yet for this study

Summary

The interest of health databases in anesthesia is no longer to be demonstrated. The aim of this research was to develop a natural language processing approach to establish a classification of adverse events observed during the perioperative period and to facilitate their analysis:

The main objective of the study was to identify what a "naïve" unsupervised model would discover based on Adverse Event (AE) descriptions. Our second goal was to identify apparently unrelated events whose combination could favor the occurrence of an AE

Conditions

  • Allergic Reaction

Sponsors & Collaborators

  • University Hospital, Strasbourg, France

    lead OTHER

Principal Investigators

  • Paul-Michel MERTES, MD, PhD · Service d'Anesthésie et Réanimation chirurgicale - CHU de Strasbourg - France

Eligibility

Min Age
1 Year
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-11-12
Primary Completion
2021-10-12
Completion
2021-11-12

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05185479 on ClinicalTrials.gov