title: Predicting Discharge Dates From the NICU Using Progress Note Data.
abstract: OBJECTIVE
  Discharging patients from the NICU may be delayed for nonmedical reasons including the need for medical equipment, parental education, and children's services. We describe a method to predict which patients will be medically ready for discharge in the next 2 to 10 days, providing lead time to address nonmedical reasons for delayed discharge.
  
  
  METHODS
  A retrospective study examined 26 features (17 extracted, 9 engineered) from daily progress notes of 4693 patients (103,206 patient-days) from the NICU of a large, academic children's hospital. These data were used to develop a supervised machine learning problem to predict days to discharge (DTD). Random forest classifiers were trained by using examined features and International Classification of Diseases, Ninth Revision-based subpopulations to determine the most important features.
  
  
  RESULTS
  Three of the 4 subpopulations (premature, cardiac, gastrointestinal surgery) and all patients combined performed similarly at 2, 4, 7, and 10 DTD with area under the curve (AUC) ranging from 0.854 to 0.865 at 2 DTD and 0.723 to 0.729 at 10 DTD. Patients undergoing neurosurgery performed worse at every DTD measure, scoring 0.749 at 2 DTD and 0.614 at 10 DTD. This model was also able to identify important features and provide "rule-of-thumb" criteria for patients close to discharge. By using DTD equal to 4 and 2 features (oral percentage of feedings and weight), we constructed a model with an AUC of 0.843.
  
  
  CONCLUSIONS
  Using clinical features from daily progress notes provides an accurate method to predict when patients in the NICU are nearing discharge.
authors.affiliation: Departments of Biomedical Informatics, and michael.w.temple@vanderbilt.edu mtemple1@me.com.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Temple
authors.affiliation: Departments of Biomedical Informatics, and Pediatrics, Vanderbilt University, Nashville, Tennessee.
authors.forename: Christoph U
authors.initials: CU
authors.lastname: Lehmann
authors.affiliation: Departments of Biomedical Informatics, and.
authors.forename: Daniel
authors.initials: D
authors.lastname: Fabbri
mesh_terms: D005544:Forecasting; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D008499:Medical Records; D010351:Patient Discharge / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D013997:Time Factors