Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data.

ABSTRACT:

Rationale

Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.

Objectives

We aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.

Methods

This observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total

SUBMITTER: Rojas JC 

PROVIDER: S-EPMC6207111 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature