Cyrielle Brossard, Christophe Goetz, Pierre Catoire, Lauriane Cipolat, Christophe Guyeux, Cédric Gil Jardine, Mahuna Akplogan, Laure Abensur Vuillaume
{"title":"使用机器学习算法预测急诊科入院:回顾性研究的概念证明。","authors":"Cyrielle Brossard, Christophe Goetz, Pierre Catoire, Lauriane Cipolat, Christophe Guyeux, Cédric Gil Jardine, Mahuna Akplogan, Laure Abensur Vuillaume","doi":"10.1186/s12873-024-01141-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.</p><p><strong>Aim: </strong>The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.</p><p><strong>Methods: </strong>We performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results.</p><p><strong>Results: </strong>The arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. Through the development of two models (one for each location), we found that the XGBoost method with hyperparameter adaptations was the best, suggesting that the studied data could be predicted (mean absolute error) at 2.63 for Hospital 1 and 2.64 for Hospital 2).</p><p><strong>Conclusions: </strong>This study ran the construction and validation of a powerful tool for predicting ED admissions in 2 French ED. This type of tool should be integrated into the overall organization of an ED, to optimize the resources of healthcare professionals.</p>","PeriodicalId":9002,"journal":{"name":"BMC Emergency Medicine","volume":"25 1","pages":"3"},"PeriodicalIF":2.3000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706136/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.\",\"authors\":\"Cyrielle Brossard, Christophe Goetz, Pierre Catoire, Lauriane Cipolat, Christophe Guyeux, Cédric Gil Jardine, Mahuna Akplogan, Laure Abensur Vuillaume\",\"doi\":\"10.1186/s12873-024-01141-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.</p><p><strong>Aim: </strong>The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.</p><p><strong>Methods: </strong>We performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results.</p><p><strong>Results: </strong>The arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. Through the development of two models (one for each location), we found that the XGBoost method with hyperparameter adaptations was the best, suggesting that the studied data could be predicted (mean absolute error) at 2.63 for Hospital 1 and 2.64 for Hospital 2).</p><p><strong>Conclusions: </strong>This study ran the construction and validation of a powerful tool for predicting ED admissions in 2 French ED. This type of tool should be integrated into the overall organization of an ED, to optimize the resources of healthcare professionals.</p>\",\"PeriodicalId\":9002,\"journal\":{\"name\":\"BMC Emergency Medicine\",\"volume\":\"25 1\",\"pages\":\"3\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706136/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Emergency Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12873-024-01141-4\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EMERGENCY MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Emergency Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12873-024-01141-4","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EMERGENCY MEDICINE","Score":null,"Total":0}
Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.
Introduction: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.
Aim: The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.
Methods: We performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results.
Results: The arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. Through the development of two models (one for each location), we found that the XGBoost method with hyperparameter adaptations was the best, suggesting that the studied data could be predicted (mean absolute error) at 2.63 for Hospital 1 and 2.64 for Hospital 2).
Conclusions: This study ran the construction and validation of a powerful tool for predicting ED admissions in 2 French ED. This type of tool should be integrated into the overall organization of an ED, to optimize the resources of healthcare professionals.
期刊介绍:
BMC Emergency Medicine is an open access, peer-reviewed journal that considers articles on all urgent and emergency aspects of medicine, in both practice and basic research. In addition, the journal covers aspects of disaster medicine and medicine in special locations, such as conflict areas and military medicine, together with articles concerning healthcare services in the emergency departments.