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AI-Driven Prediction of Hospital-Acquired Infections With EHR

NCT06791382 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000000

Last updated 2025-04-17

No results posted yet for this study

Summary

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing infection, leveraging multimodal health data.

Conditions

  • Hospital-acquired Infections

Interventions

DIAGNOSTIC_TEST

AI-Based Diagnostic and Prognostic Model

This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, clinical observations, and treatment data, to predict the risk of hospital-acquired infections (HAIs). The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of patients at risk for infections. By analyzing historical health data, the model aims to predict potential infection developments, improving early detection, prevention strategies, and patient outcomes in hospital settings.

Sponsors & Collaborators

  • The Eye Hospital of Wenzhou Medical University

    lead OTHER

Eligibility

Min Age
0 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-02-01
Primary Completion
2025-05-31
Completion
2025-05-31

Countries

  • China

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 NCT06791382 on ClinicalTrials.gov