Michael W Temple1, Christoph U Lehmann2, Daniel Fabbri3. 1. Departments of Biomedical Informatics, and michael.w.temple@vanderbilt.edu mtemple1@me.com. 2. Departments of Biomedical Informatics, and Pediatrics, Vanderbilt University, Nashville, Tennessee. 3. Departments of Biomedical Informatics, and.
Abstract
BACKGROUND AND OBJECTIVES: 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.
BACKGROUND AND OBJECTIVES: 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: Jeffrey D Edwards; Amy J Houtrow; Eduard E Vasilevskis; Roberta S Rehm; Barry P Markovitz; Robert J Graham; R Adams Dudley Journal: Crit Care Med Date: 2012-07 Impact factor: 7.598
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