Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol.
<h4>Introduction</h4>Machine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. This paper presents a protocol for a scoping review, w...
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294231&type=printable |
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| author | Fiona Leonard Dympna O'Sullivan John Gilligan Nicola O'Shea Michael J Barrett |
| author_facet | Fiona Leonard Dympna O'Sullivan John Gilligan Nicola O'Shea Michael J Barrett |
| author_sort | Fiona Leonard |
| collection | DOAJ |
| description | <h4>Introduction</h4>Machine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. This paper presents a protocol for a scoping review, with the objective of summarising the existing research on machine learning clinical decision support system tools in the emergency department, focusing on models that can be used for paediatric patients, where a knowledge gap exists.<h4>Materials and methods</h4>The methodology used will follow the scoping study framework of Arksey and O'Malley, along with other guidelines. Machine learning clinical decision support system tools for any outcome and population (paediatric/adult/mixed) for use in the emergency department will be included. Articles such as grey literature, letters, pre-prints, editorials, scoping/literature/narrative reviews, non-English full text papers, protocols, surveys, abstract or full text not available and models based on synthesised data will be excluded. Articles from the last five years will be included. Four databases will be searched: Medline (EBSCO), CINAHL (EBSCO), EMBASE and Cochrane Central. Independent reviewers will perform the screening in two sequential stages (stage 1: clinician expertise and stage 2: computer science expertise), disagreements will be resolved by discussion. Data relevant to the research question will be collected. Quantitative analysis will be performed to generate the results.<h4>Discussion</h4>The study results will summarise the existing research on machine learning clinical decision support tools in the emergency department, focusing on models that can be used for paediatric patients. This holds the promise to identify opportunities to both incorporate models in clinical practice and to develop future models by utilising reviewers from diverse backgrounds and relevant expertise. |
| format | Article |
| id | doaj-art-1fd8b07778a14454b58dc1e04d9716ad |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1fd8b07778a14454b58dc1e04d9716ad2025-02-05T05:32:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011811e029423110.1371/journal.pone.0294231Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol.Fiona LeonardDympna O'SullivanJohn GilliganNicola O'SheaMichael J Barrett<h4>Introduction</h4>Machine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. This paper presents a protocol for a scoping review, with the objective of summarising the existing research on machine learning clinical decision support system tools in the emergency department, focusing on models that can be used for paediatric patients, where a knowledge gap exists.<h4>Materials and methods</h4>The methodology used will follow the scoping study framework of Arksey and O'Malley, along with other guidelines. Machine learning clinical decision support system tools for any outcome and population (paediatric/adult/mixed) for use in the emergency department will be included. Articles such as grey literature, letters, pre-prints, editorials, scoping/literature/narrative reviews, non-English full text papers, protocols, surveys, abstract or full text not available and models based on synthesised data will be excluded. Articles from the last five years will be included. Four databases will be searched: Medline (EBSCO), CINAHL (EBSCO), EMBASE and Cochrane Central. Independent reviewers will perform the screening in two sequential stages (stage 1: clinician expertise and stage 2: computer science expertise), disagreements will be resolved by discussion. Data relevant to the research question will be collected. Quantitative analysis will be performed to generate the results.<h4>Discussion</h4>The study results will summarise the existing research on machine learning clinical decision support tools in the emergency department, focusing on models that can be used for paediatric patients. This holds the promise to identify opportunities to both incorporate models in clinical practice and to develop future models by utilising reviewers from diverse backgrounds and relevant expertise.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294231&type=printable |
| spellingShingle | Fiona Leonard Dympna O'Sullivan John Gilligan Nicola O'Shea Michael J Barrett Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. PLoS ONE |
| title | Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. |
| title_full | Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. |
| title_fullStr | Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. |
| title_full_unstemmed | Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. |
| title_short | Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. |
| title_sort | supporting clinical decision making in the emergency department for paediatric patients using machine learning a scoping review protocol |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294231&type=printable |
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