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Optimized Multi-modality Machine Learning Approach During Cardio-toxic Chemotherapy to Predict Arising Heart Failure

NCT02934971 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 470

Last updated 2016-10-17

No results posted yet for this study

Summary

The present project will develop an automated machine learning approach using multi-modality data (imaging, laboratory, electrocardiography and questionnaire) to increase the understanding and prediction of arising heart failure in patients scheduled for cardio-toxic chemotherapy. This algorithmus will be developed by the technical cooperation partner at Technion, the institut for biomedical engineering in Haifa, Israel.

Conditions

  • Toxicity Due to Chemotherapy

Sponsors & Collaborators

  • Technion, Israel Institute of Technology

    collaborator OTHER

  • RWTH Aachen University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
100 Years
Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2017-01-31
Primary Completion
2019-01-31
Completion
2019-01-31

Countries

  • Germany

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 NCT02934971 on ClinicalTrials.gov