Prediction of Expected Length of Hospital Stay Using Machine Learning

Brigham and Women's Hospital500 enrolled

Overview

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 length of stay throughout a patient's admission. This algorithm was then validated in a validation cohort.

Study Type

OBSERVATIONAL

Enrollment

500

Conditions

Infection Heart Failure Chronic Obstructive Pulmonary Disease Asthma Gout Flare Chronic Kidney Diseases Hypertensive Urgency Atrial Fibrillation Rapid

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.

Locations (2)

Brigham and Women's Hospital

Boston, Massachusetts, United States

Brigham and Women's Faulkner Hospital

Boston, Massachusetts, United States

Outcomes

Primary Outcomes

Length of Stay

The time spent by each patient in Home Hospital from time of admission to time of discharge, measured in hours

Time frame: From date of admission to date of discharge (1 to 24 days)

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.