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Machine Learning to Predict Acute Care During Cancer Therapy

NCT05122247 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 12000

Last updated 2023-09-21

No results posted yet for this study

Summary

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.

Conditions

  • Chemotherapeutic Toxicity

Interventions

OTHER

Machine learning algorithm

machine learning directed identification of chemotherapy patients at high-risk for emergency department acute care and/or hospitalization

Sponsors & Collaborators

Principal Investigators

  • Manisha Palta, MD · Duke Health

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-01-03
Primary Completion
2023-09-19
Completion
2023-09-19

Countries

  • United States

Study Locations

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Entities

Companies

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 NCT05122247 on ClinicalTrials.gov