Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning models Article Swipe
Sebastian Germer
,
Christiane Rudolph
,
Louisa Labohm
,
Alexander Katalinic
,
Natalie Rath
,
Katharina Rausch
,
Bernd Holleczek
,
Heinz Handels
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijmedinf.2024.105607
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijmedinf.2024.105607
The studied methods are highly relevant for epidemiological researchers to create more accurate survival models, which can help physicians make informed decisions about appropriate therapies and management of patients with lung cancer, ultimately improving survival and quality of life.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ijmedinf.2024.105607
- OA Status
- hybrid
- Cited By
- 8
- References
- 26
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4401875620Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ijmedinf.2024.105607Digital Object Identifier
- Title
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Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning modelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-26Full publication date if available
- Authors
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Sebastian Germer, Christiane Rudolph, Louisa Labohm, Alexander Katalinic, Natalie Rath, Katharina Rausch, Bernd Holleczek, Heinz HandelsList of authors in order
- Landing page
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https://doi.org/10.1016/j.ijmedinf.2024.105607Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.ijmedinf.2024.105607Direct OA link when available
- Concepts
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Compendium, Proportional hazards model, Regression analysis, Machine learning, Cancer registry, Lung cancer, Artificial intelligence, Computer science, Regression, Survival analysis, Cancer, Medicine, Data mining, Statistics, Oncology, Internal medicine, Mathematics, History, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 7, 2024: 1Per-year citation counts (last 5 years)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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