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

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 to 2012. Interventions: None. Measurements and Main Results: Among eligible 25,947 hospital admissions, we observed 5,504 (21.2%) in which patients died or had ICU length of stay greater than or equal to 7 days. The gradient boosting machine model had the highest discrimination without (area under the receiver operating characteristic curve, 0.83; 95% CI, 0.81–0.84) and with (area under the receiver operating characteristic curve, 0.89; 95% CI, 0.88–0.90) text-derived variables. Both gradient boosting machines and random forests outperformed logistic regression without text data (p

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