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Background and Objectives Discharging patients from the Neonatal Intensive Care Unit (NICU) may be delayed for non-medical reasons including the need for medical equipment, parental education, and children’s services. We describe a method to predict and identify patients that will be medically ready for discharge in the next 2–10 days – providing lead-time to address non-medical reasons for delayed discharge. Methods A retrospective study examined 26 features (17 extracted, 9 engineered) from daily progress notes of 4,693 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 using combinations of examined features and ICD-9-based subpopulations to determine the most important features and those features which most accurately predicted days to discharge. Results Three of the four sub-populations (Premature, Cardiac, GI surgery) and all patients combined performed similarly at 2, 4, 7, and 10 DTD with AUC ranging from 0.854–0.865 at 2 DTD and 0.723–0.729 at 10 DTD. Neurosurgery patients 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. Using DTD equal to 4 and 2 features (oral percentage of feedings and weight) we constructed a model with an AUC of 0.843. Conclusion Using clinical features from daily progress notes provides an accurate method to predict when NICU patients are nearing discharge.

KeywordsNeonatal intensive care units, area under curve; patient discharge; ROC curve SummaryObjectives: 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. Discussion: 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. Conclusion: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. Background and ObjectivesApproximately four million babies are born in the United States each year and approximately 11% of those are born prematurely [1]. The cost of caring for these infants is substantial, with an estimated total annual cost of 26 billion dollars posing a significant financial burden for society in general and the health care system specifically [1]. Discharging these patients as soon as they are medically ready is critical for controlling expenditures. Delayed discharge of hospitalized patients, who are medically ready for discharge, is a common occurrence and often related to dependency and the need for post-discharge services [2]. Neonates discharged from the NICU -whether they are premature or recovering from another conditionare prime examples of patients with dependencies on parents and caregivers, who rely heavily on post-discharge services for medical follow-up, home medical equipment, and home nursing [3]. Parents of these fragile infants require a significant amount of training and education regarding the special needs of their newborn, the use of medical equipment, and medication administration. Infants often require a number of services at the end of their hospitalization that may ...

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