Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
With the increasing global demand for artificial intelligence solutions, their role in medicine is also expected to grow as a result of their advantage of easy access to clinical data. Machine learning models, with their ability to process large amounts of data, can help solve clinical issues. The aim of this study was to construct seven machine learning models to predict the outcomes of emergency department patients and compare their prediction performance. Data from 75,803 visits to the emergency department of a public hospital between January 2022 to December 2023 were retrospectively collected. The final dataset incorporated 34 predictors, including two sociodemographic factors, 23 laboratory variables, five initial vital signs, and four emergency department-related variables. They were used to predict the outcomes (mortality, referral, discharge, and hospitalization). During the study period, 316 (0.4%) visits ended in mortality, 5285 (7%) in referral, 13,317 (17%) in hospitalization, and 56,885 (75%) in discharge. The disposition accuracy (sensitivity and specificity) was evaluated using 34 variables for seven machine learning tools according to the area under the curve (AUC). The AUC scores were 0.768, 0.694, 0.829, 0.879, 0.892, 0.923, and 0.958 for Adaboost, logistic regression, K-nearest neighbor, LightGBM, CatBoost, XGBoost, and Random Forest (RF) models, respectively. The machine learning models, especially the discrimination ability of the RF model, were much more reliable in predicting the clinical outcomes in the emergency department. XGBoost and CatBoost ranked second and third, respectively, following RF modeling.
Açıklama
Anahtar Kelimeler
emergency department, machine learning, modeling, artificial intelligence, prediction
Kaynak
Applied Sciences-Basel
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
15
Sayı
3
