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AI4Triage - Development of an Artificial Intelligence Based Methods for the Analysis of Triage Data.

NCT07312968 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2026-01-06

No results posted yet for this study

Summary

Artificial intelligence, and in particular Graph Neural Networks (GNNs), have shown enormous potential in the analysis of complex clinical data. Thanks to their ability to model relationships between variables, GNNs represent a significant evolution compared to traditional models, enabling better interpretation of medical information and supporting data-driven decision-making in complex contexts such as emergency medicine.

The application of GNNs to clinical triage and to the prediction of length of stay can improve clinical efficiency by optimizing resource allocation and patient management. This observational study aims to evaluate the accuracy of predictions with respect to real clinical data, contributing to the development of advanced predictive tools to support healthcare decision-making processes.

Conditions

  • Triage

Interventions

OTHER

Observation

there is no intervetiuons

Sponsors & Collaborators

  • Azienda Ospedaliero Universitaria Renato Dulbecco

    collaborator UNKNOWN

  • University of Catanzaro

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-11-01
Primary Completion
2026-11-30
Completion
2027-11-30

Countries

  • Italy

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 NCT07312968 on ClinicalTrials.gov