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Correction: A hybrid machine learning framework to improve prediction of all-cause rehospitalization among eldely patients in Hong Kong

BMC Medical Research Methodology volume 23, Article number: 38 (2023) Cite this article

The Original Article was published on 13 January 2023

Correction: BMC Medical Research Methodology 23, 14 (2023)

https://doi.org/10.1186/s12874-022-01824-1

Following publication of the original article [1], Fig. 4 in the original version of this article has been replaced.

The original article has been updated.

Reference

  1. Guan J, Leung E, Kwok K, et al. A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong. BMC Medical Research Methodology. 2023;23:14. https://doi.org/10.1186/s12874-022-01824-1

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Author notes

  1. Jingjing Guan and Eman Leung are joint first-author.

Authors and Affiliations

  1. Epitelligence, Hong Kong SAR, China

    Jingjing Guan

  2. JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China

    Eman Leung & Kin-on Kwok

  3. Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong SAR, China

    Kin-on Kwok

  4. Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, Hong Kong SAR, China

    Kin-on Kwok

  5. Department of Management Sciences, City University of Hong Kong, Hong Kong SAR, China

    Frank Youhua Chen

Authors
  1. Jingjing Guan
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  2. Eman Leung
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  3. Kin-on Kwok
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  4. Frank Youhua Chen
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Corresponding author

Correspondence to Frank Youhua Chen.

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Guan, J., Leung, E., Kwok, Ko. et al. Correction: A hybrid machine learning framework to improve prediction of all-cause rehospitalization among eldely patients in Hong Kong. BMC Med Res Methodol 23, 38 (2023). https://doi.org/10.1186/s12874-023-01851-6

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  • DOI: https://doi.org/10.1186/s12874-023-01851-6

BMC Medical Research Methodology

ISSN: 1471-2288

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