ArticleApplied clinical informatics2016
Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.
Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Exploring timely and safe discharge from ICU: a comparative study of machine learning predictions and clinical practices.Intensive care medicine experimental · 2025Article
- Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients.Applied clinical informatics · 2024Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Improving Pediatric Intensive Care Unit Discharge Timeliness of Infants with Bronchiolitis Using Clinical Decision Support.Applied clinical informatics · 2023Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Provider Networks in the Neonatal Intensive Care Unit Associate with Length of Stay.... IEEE Conference on Collaboration and Internet Computing. IEEE Conference on Collaboration and Internet Computing · 2019Article
- Computerized Physician Order Entry in the Neonatal Intensive Care Unit: A Narrative Review.Applied clinical informatics · 2019Review
- Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.Yearbook of medical informatics · 2017Review
- Evaluation of two large language models for intensive care unit discharge decisions: a prospective observational cohort study.Brazilian journal of anesthesiology (Elsevier)Observational
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Authors and funding
3 authors.
Funding
Abstract
objectivesDischarging 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.
methodsWe 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.
resultsNLP 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.
conclusionA BOW analysis provides a method to improve and refine our NICU discharge prediction model and could potentially avoid over 900 (0.9%) hospital days.
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