Analysis of Adverse Events in Anesthesia Using Artificial Intelligence

University Hospital, Strasbourg, France9,559 enrolled

Overview

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

Study Type

OBSERVATIONAL

Enrollment

9,559

Conditions

Allergic Reaction

Eligibility

Sex: ALLMin age: 1 Year

Medical Language ↔ Plain English

Inclusion Criteria: * Minors and adults having had an allergic reaction associated with care * Having had an adverse event reported by an anesthetist between January 01, 2009 and June 30, 2020 Exclusion Criteria: \- Patient not meeting the inclusion criteria

Locations (1)

Service d'Anesthésie et Réanimation chirurgicale - CHU de Strasbourg - France

Strasbourg, France

Outcomes

Primary Outcomes

Development of a natural language processing approach to establish a classification of adverse events observed during the perioperative period and to facilitate their analysis.

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

Time frame: Files analysed retrospectively from January 01, 2009 to June 30, 2020 will be examined]

Data from ClinicalTrials.gov

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