title: Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.
abstract: OBJECTIVE
  Discharging patients from the Neonatal Intensive Care Unit (NICU) can be delayed for non-medical reasons including the procurement of home medical equipment, parental education, and the need for children's services. We previously created a model to identify patients that will be medically ready for discharge in the subsequent 2-10 days. In this study we use Natural Language Processing to improve upon that model and discern why the model performed poorly on certain patients.
  
  
  METHODS
  We retrospectively examined the text of the Assessment and Plan section from daily progress notes of 4,693 patients (103,206 patient-days) from the NICU of a large, academic children's hospital. A matrix was constructed using words from NICU notes (single words and bigrams) to train a supervised machine learning algorithm to determine the most important words differentiating poorly performing patients compared to well performing patients in our original discharge prediction model.
  
  
  RESULTS
  NLP using a bag of words (BOW) analysis revealed several cohorts that performed poorly in our original model. These included patients with surgical diagnoses, pulmonary hypertension, retinopathy of prematurity, and psychosocial issues.
  
  
  CONCLUSIONS
  The BOW approach aided in cohort discovery and will allow further refinement of our original discharge model prediction. Adequately identifying patients discharged home on g-tube feeds alone could improve the AUC of our original model by 0.02. Additionally, this approach identified social issues as a major cause for delayed discharge.
  
  
  CONCLUSIONS
  A BOW analysis provides a method to improve and refine our NICU discharge prediction model and could potentially avoid over 900 (0.9%) hospital days.
authors.affiliation: Department of Biomedical Informatics Vanderbilt University , Nashville, TN.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Temple
authors.affiliation: Department of Biomedical Informatics Vanderbilt University, Nashville, TN; Department of Pediatrics Vanderbilt University, Nashville, TN.
authors.forename: Christoph U
authors.initials: CU
authors.lastname: Lehmann
authors.affiliation: Department of Biomedical Informatics Vanderbilt University , Nashville, TN.
authors.forename: Daniel
authors.initials: D
authors.lastname: Fabbri
mesh_terms: D015331:Cohort Studies; D057225:Data Mining; D006785:Hospitals, University / Q000706:statistics & numerical data; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000706:statistics & numerical data*; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D009323:Natural Language Processing; D010351:Patient Discharge / Q000706:statistics & numerical data*; D011336:Probability; D012189:Retrospective Studies
keywords: Neonatal intensive care units; ROC curve; area under curve; patient discharge