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

> **NCT02934971** · — · UNKNOWN · sponsor: **RWTH Aachen University** · enrollment: 470 (estimated)

## Conditions studied

- Toxicity Due to Chemotherapy

## Interventions

_None listed._

## Key facts

- **NCT ID:** NCT02934971
- **Lead sponsor:** RWTH Aachen University
- **Sponsor class:** OTHER
- **Phase:** —
- **Study type:** OBSERVATIONAL
- **Status:** UNKNOWN
- **Start date:** 2017-01
- **Primary completion:** 2019-01
- **Final completion:** 2019-01
- **Target enrollment:** 470 (ESTIMATED)
- **Last updated:** 2016-10-17

## Collaborators

- [object Object]

## Primary source

ClinicalTrials.gov registry: https://clinicaltrials.gov/study/NCT02934971

## Citation

> US National Library of Medicine, ClinicalTrials.gov registration NCT02934971, "Optimized Multi-modality Machine Learning Approach During Cardio-toxic Chemotherapy to Predict Arising Heart Failure". Retrieved via AI Analytics 2026-06-02 from https://api.ai-analytics.org/clinical/NCT02934971. Licensed CC0.

---

*[Clinical trials dataset](/datasets/clinical-trials) · CC0 1.0*