Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
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 a...
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MDPI AG
2025-02-01
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| author | Savaş Sezik Mustafa Özgür Cingiz Esma İbiş |
| author_facet | Savaş Sezik Mustafa Özgür Cingiz Esma İbiş |
| author_sort | Savaş Sezik |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b8144c052dbb4063bcfe9aa0633565a0 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-b8144c052dbb4063bcfe9aa0633565a02025-08-20T02:48:02ZengMDPI AGApplied Sciences2076-34172025-02-01153162810.3390/app15031628Machine Learning-Based Model for Emergency Department Disposition at a Public HospitalSavaş Sezik0Mustafa Özgür Cingiz1Esma İbiş2Division of Emergency Medicine, Odemiş State Hospital, 35750 Izmir, TürkiyeDepartment of Computer Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, 16310 Bursa, TürkiyeDepartment of Computer Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, 16310 Bursa, TürkiyeWith 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.https://www.mdpi.com/2076-3417/15/3/1628emergency departmentmachine learningmodelingartificial intelligenceprediction |
| spellingShingle | Savaş Sezik Mustafa Özgür Cingiz Esma İbiş Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital Applied Sciences emergency department machine learning modeling artificial intelligence prediction |
| title | Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital |
| title_full | Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital |
| title_fullStr | Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital |
| title_full_unstemmed | Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital |
| title_short | Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital |
| title_sort | machine learning based model for emergency department disposition at a public hospital |
| topic | emergency department machine learning modeling artificial intelligence prediction |
| url | https://www.mdpi.com/2076-3417/15/3/1628 |
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