Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.

Journal: Applied clinical informatics

Published Date: Jan 1, 2016

Abstract

OBJECTIVES: 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.

Authors

Michael W Temple

Department of Biomedical Informatics Vanderbilt University , Nashville, TN.
Christoph U Lehmann

Department of Biomedical Informatics Vanderbilt University, Nashville, TN; Department of Pediatrics Vanderbilt University, Nashville, TN.
Daniel Fabbri

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.

Keywords

Cohort Studies Data Mining Hospitals, University Humans Infant, Newborn Intensive Care Units, Neonatal Medical Informatics Models, Statistical Natural Language Processing Patient Discharge Probability Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (27081410)

Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.

Abstract

Authors

Keywords

External Resources

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