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Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm

NCT06796283 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2628

Last updated 2025-02-21

No results posted yet for this study

Summary

The purpose of this observational study is to construct a recurrence risk prediction model for ischemic stroke within 1, 3, 6, and 12 months using XGBoost combined with Convolutional Neural Network (CNN) algorithm.

Method: Follow up was conducted on the study subjects at 1, 3, 6, and 12 months after discharge.

Follow up primary outcome: Whether the study subjects experienced recurrent stroke events.

Secondary outcome: Improved Rinkin score.

Collect information on research subjects:

It includes demographic data, physical examination, medical history, imaging images, medication use, scale scores, CYP2C19 genotype test results, laboratory tests, and other complex multidimensional data.

Conditions

Sponsors & Collaborators

  • Second Affiliated Hospital of Nanchang University

    lead OTHER

Principal Investigators

  • yingping Y Yi · Second Affiliated Hospital of Nanchang University

Eligibility

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

Timeline & Regulatory

Start
2021-04-26
Primary Completion
2023-03-04
Completion
2024-12-31

Countries

  • China

Study Locations

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Entities

Diseases

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 NCT06796283 on ClinicalTrials.gov