Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.

ABSTRACT:

Background

Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions.

Methods

We conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived

SUBMITTER: Lo YT 

PROVIDER: S-EPMC8527795 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature