Corrigendum to “Machine learning computational model to predict lung cancer using electronic medical records”. Journal: Cancer Epidemiology, volume 92 (2024) (Cancer Epidemiology (2024) 92, (S1877782124001103), (10.1016/j.canep.2024.102631))

Matanel Levi; Teddy Lazebnik; Shiri Kushnir; Noga Yosef; Dekel Shlomi

doi:10.1016/j.canep.2024.102649

Corrigendum to “Machine learning computational model to predict lung cancer using electronic medical records”. Journal: Cancer Epidemiology, volume 92 (2024) (Cancer Epidemiology (2024) 92, (S1877782124001103), (10.1016/j.canep.2024.102631))

Matanel Levi
, Teddy Lazebnik
, Shiri Kushnir
, Noga Yosef
, Dekel Shlomi

Ariel University
University College London
Rabin Medical Center Israel
Clalit Health Services

Research output: Contribution to journal › Comment/debate

1 Scopus citations

Abstract

The authors regret and would like to correct the first and third Highlights (marked in red): • Artificial intelligence can be used to predict lung cancer by utilizing common risk factors. • For nonsmokers, an accuracy of 73 % was found for predicting lung cancer. • This study highlights the importance of each risk factor in a machine learning model. The authors would like to apologise for any inconvenience caused.

Original language	English
Article number	102649
Journal	Cancer Epidemiology
Volume	93
DOIs	https://doi.org/10.1016/j.canep.2024.102649
State	Published - Dec 2024
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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Levi, M., Lazebnik, T., Kushnir, S., Yosef, N., & Shlomi, D. (2024). Corrigendum to “Machine learning computational model to predict lung cancer using electronic medical records”. Journal: Cancer Epidemiology, volume 92 (2024) (Cancer Epidemiology (2024) 92, (S1877782124001103), (10.1016/j.canep.2024.102631)). Cancer Epidemiology, 93, Article 102649. https://doi.org/10.1016/j.canep.2024.102649

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title = "Corrigendum to “Machine learning computational model to predict lung cancer using electronic medical records”. Journal: Cancer Epidemiology, volume 92 (2024) (Cancer Epidemiology (2024) 92, (S1877782124001103), (10.1016/j.canep.2024.102631))",

abstract = "The authors regret and would like to correct the first and third Highlights (marked in red): • Artificial intelligence can be used to predict lung cancer by utilizing common risk factors. • For nonsmokers, an accuracy of 73 \% was found for predicting lung cancer. • This study highlights the importance of each risk factor in a machine learning model. The authors would like to apologise for any inconvenience caused.",

author = "Matanel Levi and Teddy Lazebnik and Shiri Kushnir and Noga Yosef and Dekel Shlomi",

note = "Publisher Copyright: {\textcopyright} 2024 Elsevier Ltd",

year = "2024",

month = dec,

doi = "10.1016/j.canep.2024.102649",

language = "אנגלית",

volume = "93",

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Levi, M, Lazebnik, T, Kushnir, S, Yosef, N & Shlomi, D 2024, 'Corrigendum to “Machine learning computational model to predict lung cancer using electronic medical records”. Journal: Cancer Epidemiology, volume 92 (2024) (Cancer Epidemiology (2024) 92, (S1877782124001103), (10.1016/j.canep.2024.102631))', Cancer Epidemiology, vol. 93, 102649. https://doi.org/10.1016/j.canep.2024.102649

Corrigendum to “Machine learning computational model to predict lung cancer using electronic medical records”. Journal: Cancer Epidemiology, volume 92 (2024) (Cancer Epidemiology (2024) 92, (S1877782124001103), (10.1016/j.canep.2024.102631)). / Levi, Matanel; Lazebnik, Teddy; Kushnir, Shiri et al.
In: Cancer Epidemiology, Vol. 93, 102649, 12.2024.

Research output: Contribution to journal › Comment/debate

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AU - Levi, Matanel

AU - Lazebnik, Teddy

AU - Kushnir, Shiri

AU - Yosef, Noga

AU - Shlomi, Dekel

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N2 - The authors regret and would like to correct the first and third Highlights (marked in red): • Artificial intelligence can be used to predict lung cancer by utilizing common risk factors. • For nonsmokers, an accuracy of 73 % was found for predicting lung cancer. • This study highlights the importance of each risk factor in a machine learning model. The authors would like to apologise for any inconvenience caused.

AB - The authors regret and would like to correct the first and third Highlights (marked in red): • Artificial intelligence can be used to predict lung cancer by utilizing common risk factors. • For nonsmokers, an accuracy of 73 % was found for predicting lung cancer. • This study highlights the importance of each risk factor in a machine learning model. The authors would like to apologise for any inconvenience caused.

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SN - 1877-7821

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JO - Cancer Epidemiology

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ER -

Levi M, Lazebnik T, Kushnir S, Yosef N, Shlomi D. Corrigendum to “Machine learning computational model to predict lung cancer using electronic medical records”. Journal: Cancer Epidemiology, volume 92 (2024) (Cancer Epidemiology (2024) 92, (S1877782124001103), (10.1016/j.canep.2024.102631)). Cancer Epidemiology. 2024 Dec;93:102649. doi: 10.1016/j.canep.2024.102649

Corrigendum to “Machine learning computational model to predict lung cancer using electronic medical records”. Journal: Cancer Epidemiology, volume 92 (2024) (Cancer Epidemiology (2024) 92, (S1877782124001103), (10.1016/j.canep.2024.102631))

Abstract

Bibliographical note

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