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

Southeast University, China1,000 enrolled

Overview

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.

Build the early warning system.

Study Type

INTERVENTIONAL

Allocation

NON_RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Enrollment

1,000

Conditions

High-risk Patients Risk Reduction Machine Learning

Interventions

early warning platformDEVICE

High risk inpatients will be evaluated by early warning platform

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

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

Locations (1)

Zhongda Hospital, Southeast University

Nanjing, Jiangsu, China

RECRUITING

Outcomes

Primary Outcomes

28-day all cause mortality

Time frame: 28 days

Secondary Outcomes

Hospital mortality

Time frame: through study completion, an average of 1 month

Central Contacts

Changde Wu

CONTACT

086-02583262550 liusongqiao@ymail.com

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.