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Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

NCT03724123 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2229

Last updated 2018-10-30

No results posted yet for this study

Summary

Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this risk allows for improved counseling of patients and avoidance of possible complications. The investigators therefore investigate the benefit of modern machine learning methods in personalized risk prediction in patients undergoing elective heart valve surgery.

Conditions

  • Heart Valve Diseases
  • Surgery--Complications

Sponsors & Collaborators

  • Institute of Bioinformatics, JKU Linz

    collaborator UNKNOWN

  • Kepler University Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2008-01-01
Primary Completion
2014-12-31
Completion
2014-12-31

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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 NCT03724123 on ClinicalTrials.gov