Predicting Hospital Admission for Emergency Department Patients: A Machine Learning Approach

Abstract

The objective of this study was to establish a machine learning model and to evaluate its predictive capability of admission to the hospital. This observational retrospective study included 3204 emergency department visits to a public tertiary care hospital in Greece from 14 March to 4 May 2019. We investigated biochemical markers and coagulation tests that are routinely checked in patients visiting the Emergency Department (ED) in relation to the ED outcome (admission or discharge). Among the most popular classification techniques of the scikit-learn library through a 10-fold cross-validation approach, a GaussianNB model outperformed other models with respect to the area under the receiver operating characteristic curve.

Predicting Hospital Ward Admission from the Emergency Department: A Systematic Review

“…Of the 14 final articles, three were developed in Spain [ 17 , 22 , 23 ], three in the Netherlands [ 18 , 24 , 27 ], two in Greece [ 19 , 26 ], two in the U.K. [ 21 , 28 ], two in the USA [ 16 , 20 ], one in France [ 29 ], and 1 in Singapore [ 25 ]. Eleven of the 14 articles included as a patient any person attending the emergency department.…”

“…The second most used model was gradient boosting, in three studies [ 20 , 21 , 22 ]. Other studies used Random forest [ 19 ], Gaussian Naïve Bayes [ 26 ], gradient-powered decision tree modelling [ 18 ], and an artificial neural network [ 23 ]. All models predict ward admission from the ED, either giving a binary or probabilistic response.…”

“…One study [ 21 ] emphasized that the triage category is one of the most important factors in the prediction of admission. In 11 of the 14 articles [ 16 , 17 , 19 , 21 , 22 , 23 , 25 , 26 , 27 , 28 , 29 ], a demographic category was used, and within this category, the age variable was used in all 11 studies. Four of these articles distinguish age as a good predictor of hospital admission [ 16 , 17 , 21 , 25 ].…”

Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients

“…In the Covid-19 era, some useful applications have been introduced, such as predictive models to forecast future demands in beds, equipment, and staff [4]. Machine learning (ML) approaches might be utilized to help medical staff settle on quicker and more suitable decisions, decline superfluous testing, and reduce ED overcrowding [3][4][5]. Fast triage leads to reduced length of hospital stay, having potential economic benefits.…”

Exploratory Clustering for Emergency Department Patients

“…Recently, several studies describing effective triage prediction in ED using machine learning techniques have been published [1,2].…”