Prediction of 30-Day Readmission Using Machine Learning

Completed

Conditions studied: Infection, Heart Failure, Chronic Obstructive Pulmonary Disease, Asthma, Gout Flare, Chronic Kidney Diseases, Hypertensive Urgency, Atrial Fibrillation Rapid, Anticoagulants; Increased

In brief

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.

Key facts

Study ID
NCT04849312
Run by
Brigham and Women's Hospital
People needed
372
Starts
2017-06-01
Expected to finish
2019-11-30
Last updated by the study team
2026-03-17

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

Where it is running

Full record on ClinicalTrials.gov

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