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Prediction of 30-Day Readmission Using Machine Learning

NCT04849312 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 372

Last updated 2026-03-17

No results posted yet for this study

Summary

This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict the likelihood of 30-day readmission throughout a patient's admission. This algorithm was then validated in a validation cohort.

Conditions

Sponsors & Collaborators

  • Biofourmis Inc.

    collaborator INDUSTRY

  • Brigham and Women's Hospital

    lead OTHER

Principal Investigators

  • David Levine, MD MPH MA · Associate Physician

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2017-06-01
Primary Completion
2019-10-31
Completion
2019-11-30

Countries

  • United States

Study Locations

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Entities

Diseases

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 NCT04849312 on ClinicalTrials.gov