Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department

Sangil Lee; Avinash Reddy Mudireddy; Deepak Kumar  Pasupula; Mehul Adhaduk; E. John Barsotti; Milan Sonka; Giselle M. Statz; Tyler Bullis; Samuel L. Johnston; Aron Z. Evans; Brian Olshansky; Milena A. Gebska

doi:10.3390/jpm13010007

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Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department

Journal article

Open access

Peer reviewed

Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department

Sangil Lee, Avinash Reddy Mudireddy, Deepak Kumar Pasupula, Mehul Adhaduk, E. John Barsotti, Milan Sonka, Giselle M. Statz, Tyler Bullis, Samuel L. Johnston, Aron Z. Evans, …

Journal of personalized medicine, Vol.13(1), p.7

12/20/2022

DOI: 10.3390/jpm13010007

PMCID: PMC9864075

PMID: 36675668

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Abstract

Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from the National Emergency Department Sample (NEDS) database presenting to the ED with syncope between 2016–2019. A multilayer perceptron neural network with one hidden layer was trained and validated on this data set. Results: Receiver Operator Characteristics (ROC) were determined for each of the five ANN models with varying cutoffs for LoS. A fair area under the curve (AUC of 0.78) to good (AUC of 0.88) prediction performance was achieved based on sequential analysis at different cutoff points, starting from the same day discharge and ending at the longest analyzed cutoff LoS ≤7 days versus >7 days, accordingly. The ML algorithm showed significant sensitivity and specificity in predicting short (≤48 h) versus long (>48 h) LoS, with an AUC of 0.81. Conclusions: Using variables available to triaging ED clinicians, ML shows promise in predicting hospital LoS with fair to good performance for patients presenting with syncope.

Details

Title: Subtitle: Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department
Creators: Sangil Lee
Avinash Reddy Mudireddy
Deepak Kumar Pasupula
Mehul Adhaduk
E. John Barsotti
Milan Sonka
Giselle M. Statz
Tyler Bullis
Samuel L. Johnston
Aron Z. Evans
Brian Olshansky
Milena A. Gebska
Resource Type: Journal article
Publication Details: Journal of personalized medicine, Vol.13(1), p.7
DOI: 10.3390/jpm13010007
PMID: 36675668
PMCID: PMC9864075
NLM abbreviation: J Pers Med
ISSN: 2075-4426
eISSN: 2075-4426
Grant note: name: Iowa Initiative for Artificial Intelligence (IIAI), Carver College of Medicine Office of Research.
Language: English
Date published: 12/20/2022
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Engineering Administration; Emergency Medicine; Cardiovascular Medicine; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; General Internal Medicine; Internal Medicine; Ophthalmology and Visual Sciences
Record Identifier: 9984353989302771

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