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Research on the Development and Validation of an Early Prediction Model for Delirium

NCT07337356 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 795

Last updated 2026-01-13

No results posted yet for this study

Summary

Delirium has a high incidence rate and significantly affects patient prognosis. Diagnosis often relies on manual assessment, which is subject to strong subjectivity, high rates of missed diagnosis, and poor stability. This study employs non-contact identification technology based on machine vision analysis to quantitatively analyze characteristic biological feature data such as micro-expressions. It then investigates the correlation between these features and delirium subtypes. By integrating clinical phenotypic data and using machine learning algorithms, a multi-modal early prediction model for delirium is constructed to meet the clinical need for early warning of delirium subtypes and enhance the efficacy of delirium identification.

Conditions

  • Delirium
  • Prediction Models
  • Machine Learning

Sponsors & Collaborators

  • Ruijin Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-02-01
Primary Completion
2026-09-01
Completion
2027-02-01

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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 NCT07337356 on ClinicalTrials.gov