Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach
Abstract Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and fre...
Saved in:
Main Authors: | Eyal Klang, Benjamin R. Kummer, Neha S. Dangayach, Amy Zhong, M. Arash Kia, Prem Timsina, Ian Cossentino, Anthony B. Costa, Matthew A. Levin, Eric K. Oermann |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/bd3eaf6342be421885a39e0d850a360c |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Managing patient expectations at emergency department triage
by: Shital Shah, et al.
Published: (2015) -
Synergistic effect of hypoalbuminaemia and hypotension in predicting in-hospital mortality and intensive care admission: a retrospective cohort study
by: Eyal Klang, et al.
Published: (2021) -
Triaging and referring in adjacent general and emergency departments (the TRIAGE trial): A cluster randomised controlled trial.
by: Stefan Morreel, et al.
Published: (2021) -
Triaging and referring in adjacent general and emergency departments (the TRIAGE trial): A cluster randomised controlled trial
by: Stefan Morreel, et al.
Published: (2021) -
Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study
by: Eyal Klang, et al.
Published: (2021)