Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.

ABSTRACT:

Objectives

Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, random forest, and elastic net regression models, with and without unstructured clinical text data, to predict a binary composite outcome of in-hospital death or ICU length of stay greater than or equal to 7 days using data from the first 48 hours of hospitalization.

Design

Retrospective cohort study with split sampling for model training and testing.

Setting

A single urban academic hospital.

Patients

All hospitalized patients who required ICU care at the Beth Israel Deaconess Medical Center in Boston, MA, from 2001

SUBMITTER: Weissman GE 

PROVIDER: S-EPMC6005735 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature