An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Arquivos brasileiros de oftalmologia (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-27492022005011215 |
Resumo: | ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients’ volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists’ experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx. |
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Arquivos brasileiros de oftalmologia (Online) |
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An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experienceMachine learningEmergency services, hospitalEye injuriesModels, statisticalAlgorithmsABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients’ volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists’ experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.Conselho Brasileiro de Oftalmologia2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-27492022005011215Arquivos Brasileiros de Oftalmologia n.ahead 2022reponame:Arquivos brasileiros de oftalmologia (Online)instname:Conselho Brasileiro de Oftalmologia (CBO)instacron:CBO10.5935/0004-2749.2022-0130info:eu-repo/semantics/openAccessNakayama,Luis FilipeRibeiro,Lucas ZagoRegatieri,Caio Vinicius Saitoeng2022-10-26T00:00:00Zoai:scielo:S0004-27492022005011215Revistahttp://aboonline.org.br/https://old.scielo.br/oai/scielo-oai.phpaboonline@cbo.com.br||abo@cbo.com.br1678-29250004-2749opendoar:2022-10-26T00:00Arquivos brasileiros de oftalmologia (Online) - Conselho Brasileiro de Oftalmologia (CBO)false |
dc.title.none.fl_str_mv |
An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience |
title |
An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience |
spellingShingle |
An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience Nakayama,Luis Filipe Machine learning Emergency services, hospital Eye injuries Models, statistical Algorithms |
title_short |
An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience |
title_full |
An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience |
title_fullStr |
An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience |
title_full_unstemmed |
An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience |
title_sort |
An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience |
author |
Nakayama,Luis Filipe |
author_facet |
Nakayama,Luis Filipe Ribeiro,Lucas Zago Regatieri,Caio Vinicius Saito |
author_role |
author |
author2 |
Ribeiro,Lucas Zago Regatieri,Caio Vinicius Saito |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Nakayama,Luis Filipe Ribeiro,Lucas Zago Regatieri,Caio Vinicius Saito |
dc.subject.por.fl_str_mv |
Machine learning Emergency services, hospital Eye injuries Models, statistical Algorithms |
topic |
Machine learning Emergency services, hospital Eye injuries Models, statistical Algorithms |
description |
ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients’ volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists’ experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-27492022005011215 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-27492022005011215 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/0004-2749.2022-0130 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Conselho Brasileiro de Oftalmologia |
publisher.none.fl_str_mv |
Conselho Brasileiro de Oftalmologia |
dc.source.none.fl_str_mv |
Arquivos Brasileiros de Oftalmologia n.ahead 2022 reponame:Arquivos brasileiros de oftalmologia (Online) instname:Conselho Brasileiro de Oftalmologia (CBO) instacron:CBO |
instname_str |
Conselho Brasileiro de Oftalmologia (CBO) |
instacron_str |
CBO |
institution |
CBO |
reponame_str |
Arquivos brasileiros de oftalmologia (Online) |
collection |
Arquivos brasileiros de oftalmologia (Online) |
repository.name.fl_str_mv |
Arquivos brasileiros de oftalmologia (Online) - Conselho Brasileiro de Oftalmologia (CBO) |
repository.mail.fl_str_mv |
aboonline@cbo.com.br||abo@cbo.com.br |
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1754209032518762496 |