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

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...

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Autores principales: Feretzakis, Georgios, Karlis, George, Loupelis, Evangelos, Kalles, Dimitris, Chatzikyriakou, Rea, Trakas, Nikolaos, Karakou, Eugenia, Sakagianni, Aikaterini, Tzelves, Lazaros, Petropoulou, Stavroula, Tika, Aikaterini, Dalainas, Ilias, Kaldis, Vasileios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Sciendo 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9097643/
https://www.ncbi.nlm.nih.gov/pubmed/35950158
http://dx.doi.org/10.2478/jccm-2022-0003
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author Feretzakis, Georgios
Karlis, George
Loupelis, Evangelos
Kalles, Dimitris
Chatzikyriakou, Rea
Trakas, Nikolaos
Karakou, Eugenia
Sakagianni, Aikaterini
Tzelves, Lazaros
Petropoulou, Stavroula
Tika, Aikaterini
Dalainas, Ilias
Kaldis, Vasileios
author_facet Feretzakis, Georgios
Karlis, George
Loupelis, Evangelos
Kalles, Dimitris
Chatzikyriakou, Rea
Trakas, Nikolaos
Karakou, Eugenia
Sakagianni, Aikaterini
Tzelves, Lazaros
Petropoulou, Stavroula
Tika, Aikaterini
Dalainas, Ilias
Kaldis, Vasileios
author_sort Feretzakis, Georgios
collection PubMed
description 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.
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spelling pubmed-90976432022-08-09 Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department Feretzakis, Georgios Karlis, George Loupelis, Evangelos Kalles, Dimitris Chatzikyriakou, Rea Trakas, Nikolaos Karakou, Eugenia Sakagianni, Aikaterini Tzelves, Lazaros Petropoulou, Stavroula Tika, Aikaterini Dalainas, Ilias Kaldis, Vasileios J Crit Care Med (Targu Mures) Research Article 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. Sciendo 2022-05-12 /pmc/articles/PMC9097643/ /pubmed/35950158 http://dx.doi.org/10.2478/jccm-2022-0003 Text en © 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 https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License.
spellingShingle Research Article
Feretzakis, Georgios
Karlis, George
Loupelis, Evangelos
Kalles, Dimitris
Chatzikyriakou, Rea
Trakas, Nikolaos
Karakou, Eugenia
Sakagianni, Aikaterini
Tzelves, Lazaros
Petropoulou, Stavroula
Tika, Aikaterini
Dalainas, Ilias
Kaldis, Vasileios
Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department
title Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department
title_full Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department
title_fullStr Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department
title_full_unstemmed Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department
title_short Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department
title_sort using machine learning techniques to predict hospital admission at the emergency department
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9097643/
https://www.ncbi.nlm.nih.gov/pubmed/35950158
http://dx.doi.org/10.2478/jccm-2022-0003
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