<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec id="j_jccm-2022-0003_s_007"><h3>Introduction</h3>
<p>One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare.</p>
</sec>
<sec id="j_jccm-2022-0003_s_008"><h3>Aim of the study</h3>
<p>Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting.</p>
</sec>
<sec id="j_jccm-2022-0003_s_009"><h3>Material and methods</h3>
<p>We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed.</p>
</sec>
<sec id="j_jccm-2022-0003_s_010"><h3>Results</h3>
<p>The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model.</p>
</sec>
<sec id="j_jccm-2022-0003_s_011"><h3>Conclusions</h3>
<p>Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.</p>
</sec>

</abstract>

Introduction
One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare.

Aim of the study
Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting.

Material and methods
We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed.

Results
The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model.

Conclusions
Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.

Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department

The Journal of Critical Care Medicine

This work is licensed under the Creative Commons Attribution 4.0 International License.

Introduction
One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission....

Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department

Article Category: Research Article

Publicado en línea: 12 may 2022

Páginas: 107 - 116

Recibido: 26 ago 2021

Aceptado: 04 abr 2022

DOI: https://doi.org/10.2478/jccm-2022-0003

Palabras clave
emergency department, emergency medicine, machine learning techniques, artificial intelligence, biomarkers

© 2022 Georgios Feretzakis, George Karlis, Evangelos Loupelis, Dimitris Kalles, Rea Chatzikyriakou, Nikolaos Trakas, Eugenia Karakou, Aikaterini Sakagianni, Lazaros Tzelves, Stavroula Petropoulou, Aikaterini Tika, Ilias Dalainas, Vasileios Kaldis, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department

Article Category: Research Article

Publicado en línea: 12 may 2022

Páginas: 107 - 116

Recibido: 26 ago 2021

Aceptado: 04 abr 2022

DOI: https://doi.org/10.2478/jccm-2022-0003

Palabras claveemergency department, emergency medicine, machine learning techniques, artificial intelligence, biomarkers

© 2022 Georgios Feretzakis, George Karlis, Evangelos Loupelis, Dimitris Kalles, Rea Chatzikyriakou, Nikolaos Trakas, Eugenia Karakou, Aikaterini Sakagianni, Lazaros Tzelves, Stavroula Petropoulou, Aikaterini Tika, Ilias Dalainas, Vasileios Kaldis, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
emergency department, emergency medicine, machine learning techniques, artificial intelligence, biomarkers