Research on the Development and Validation of an Early Prediction Model for Delirium
Starting soon
Conditions studied: Delirium, Prediction Models, Machine Learning
In brief
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.
Key facts
- Study ID
- NCT07337356
- Run by
- Ruijin Hospital
- People needed
- 795
- Starts
- 2026-02-01
- Expected to finish
- 2027-02-01
- Last updated by the study team
- 2026-01-13
Who can join
Age: 18 and older. Sex: any. Healthy volunteers: not accepted.
You may qualify if…
- Age ≥ 18 years, expected ICU stay ≥ 24 hours, and informed consent to participate in this study;
You may not qualify if…
- Patients with severe facial trauma/deformities that prevent complete expression acquisition, and patients with a history of emotional problems (such as anxiety, depression, etc.).
Full record on ClinicalTrials.gov
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