Advancements in predicting and modeling rare event outcomes for enhanced decision-making

Predicting rare events is a challenging task due to limited data and imbalanced datasets. This special issue explores methodological advancements in prediction and modeling for rare events. The research showcased in this issue aims to provide valuable insights and strategies to enhance the accuracy...

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Detalles Bibliográficos
Autores principales: Feng, Cindy, Li, Longhai, Xu, Chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Editorial
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585719/
https://www.ncbi.nlm.nih.gov/pubmed/37853329
http://dx.doi.org/10.1186/s12874-023-02060-x
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author Feng, Cindy
Li, Longhai
Xu, Chang
author_facet Feng, Cindy
Li, Longhai
Xu, Chang
author_sort Feng, Cindy
collection PubMed
description Predicting rare events is a challenging task due to limited data and imbalanced datasets. This special issue explores methodological advancements in prediction and modeling for rare events. The research showcased in this issue aims to provide valuable insights and strategies to enhance the accuracy of rare event prediction and modeling.
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spelling pubmed-105857192023-10-20 Advancements in predicting and modeling rare event outcomes for enhanced decision-making Feng, Cindy Li, Longhai Xu, Chang BMC Med Res Methodol Editorial Predicting rare events is a challenging task due to limited data and imbalanced datasets. This special issue explores methodological advancements in prediction and modeling for rare events. The research showcased in this issue aims to provide valuable insights and strategies to enhance the accuracy of rare event prediction and modeling. BioMed Central 2023-10-18 /pmc/articles/PMC10585719/ /pubmed/37853329 http://dx.doi.org/10.1186/s12874-023-02060-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Editorial
Feng, Cindy
Li, Longhai
Xu, Chang
Advancements in predicting and modeling rare event outcomes for enhanced decision-making
title Advancements in predicting and modeling rare event outcomes for enhanced decision-making
title_full Advancements in predicting and modeling rare event outcomes for enhanced decision-making
title_fullStr Advancements in predicting and modeling rare event outcomes for enhanced decision-making
title_full_unstemmed Advancements in predicting and modeling rare event outcomes for enhanced decision-making
title_short Advancements in predicting and modeling rare event outcomes for enhanced decision-making
title_sort advancements in predicting and modeling rare event outcomes for enhanced decision-making
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585719/
https://www.ncbi.nlm.nih.gov/pubmed/37853329
http://dx.doi.org/10.1186/s12874-023-02060-x
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