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A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model

NCT04682756 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 2500

Last updated 2020-12-31

No results posted yet for this study

Summary

Early diagnosis of NSTEMI and UA patients is mainly through the construction of machine learning model.

Conditions

  • NSTEMI - Non-ST Segment Elevation MI
  • Unstable Angina

Interventions

DIAGNOSTIC_TEST

The model of machine learning

Early diagnosis of NTEMI patients by machine learning model

Sponsors & Collaborators

  • Shihezi University

    collaborator OTHER

  • First Affiliated Hospital of Xinjiang Medical University

    lead OTHER

Principal Investigators

  • Aikeliyaer Ainiwaer, M.D · First Affiliated Hospital of Xinjiang Medical University

  • Quan Qi, Ph.D · College of Information and Technology, Shihezi University

  • Yi Ying Du, M.D · First Affiliated Hospital of Xinjiang Medical University

Eligibility

Min Age
18 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-12-20
Primary Completion
2021-12-20
Completion
2022-06-01

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 NCT04682756 on ClinicalTrials.gov