Machine Learning-based Early Clinical Warning of High-risk Patients

Southeast University, China

Status: Enrolling

Conditions: High-risk Patients; Machine Learning; Risk Reduction

Treatments: Device: early warning platform

Study type: Interventional

Funder types: Other

Identifiers

NCT05410171

2021ZDSYLL346-P01

Details and patient eligibility

About

Through the early warning platform for inpatients established by our hospital, the various indicators of patients collected in real time are carried out for automated intelligent evaluation and analysis, early warning of high-risk patients to assess the impact on patient prognosis and the impact on the occurrence of adverse events in inpatients.

Full description

Build the early warning system.

Enrollment: 1,000 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Patients who use ECG monitoring
2. Age ≥ 18 years old
3. Understand and sign an informed consent form

Exclusion criteria

• Pregnancy or lactation

Trial design

Primary purpose: Health Services Research

Allocation: Non-Randomized

Interventional model: Sequential Assignment

Masking: None (Open label)

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1,000 participants in 2 patient groups

AI group

Experimental group

Description:

patients evaluated by early warning platform

Treatment:

Device: early warning platform

usual care group

No Intervention group

Description:

patients not evaluated by early warning platform