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NCT05122247: Chemo-SHIELD

Machine Learning to Predict Acute Care During Cancer Therapy

Completed Last updated 21 September 2023
What this trial tests

trial testing Machine learning algorithm in Chemotherapeutic Toxicity in 12,000 participants. Completed in 19 September 2023.

Timeline
3 January 2022
Primary endpoint
19 September 2023
19 September 2023

Quick facts

Lead sponsorDuke University
StatusCompleted
Study typeOBSERVATIONAL
Enrollment12,000
Start date3 January 2022
Primary completion19 September 2023
Estimated completion19 September 2023
Sites1 location across United States

Drugs / interventions tested

Conditions studied

Sponsor

Duke University

Who can join

18 and older, any sex, with Chemotherapeutic Toxicity. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

The objective of this study is to apply a validated machine-learning based model (SHIELD-RT, NCT04277650) to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters.

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

  1. Artificial Intelligence in Oncology: A 10-Year ClinicalTrials.gov-Based Analysis Across the Cancer Control Continuum.
    Verma H, Mistry S, Jayam KV, Shrestha P, et al · · 2025 · PMID 41228330 · DOI 10.3390/cancers17213537

Verify or expand the search:

Other trials of Machine learning algorithm

Trials testing the same drug.

Other recruiting trials for Chemotherapeutic Toxicity

Currently open trials in the same condition.

Other Duke University trials

Trials by the same sponsor.

Verify against primary sources

Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT05122247.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing