A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost ) Artificial Intelligence Algorithm for Drug Development Applications
Article Swipe
Matthew Wiens
,
Alissa Verone‐Boyle
,
Nick Henscheid
,
Jagdeep T. Podichetty
,
Jackson Burton
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/cts.70172
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/cts.70172
Approaches to artificial intelligence and machine learning (AI/ML) continue to advance in the field of drug development. A sound understanding of the underlying concepts and guiding principles of AI/ML implementation is a prerequisite to identifying which AI/ML approach is most appropriate based on the context. This tutorial focuses on the concepts and implementation of the popular eXtreme gradient boosting (XGBoost) algorithm for classification and regression of simple clinical trial‐like datasets. Emphasis is placed on relating the underlying concepts to the code implementation. In doing so, the aim is for the reader to gain knowledge about the underlying algorithm and become better versed with how to implement the algorithm functions for relevant clinical drug development questions. In turn, this will provide practical ML experience which can be applied to algorithms and problems beyond the scope of this tutorial.
Related Topics
Concepts
Computer science
Boosting (machine learning)
Artificial intelligence
Machine learning
Algorithm
Scope (computer science)
Field (mathematics)
Drug development
Gradient boosting
Context (archaeology)
Drug
Medicine
Mathematics
Biology
Pure mathematics
Random forest
Paleontology
Psychiatry
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/cts.70172
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cts.70172
- OA Status
- gold
- Cited By
- 18
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408309490
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408309490Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/cts.70172Digital Object Identifier
- Title
-
A Tutorial and Use Case Example of the
eXtreme Gradient Boosting (XGBoost ) Artificial Intelligence Algorithm for Drug Development ApplicationsWork title - Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-01Full publication date if available
- Authors
-
Matthew Wiens, Alissa Verone‐Boyle, Nick Henscheid, Jagdeep T. Podichetty, Jackson BurtonList of authors in order
- Landing page
-
https://doi.org/10.1111/cts.70172Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cts.70172Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cts.70172Direct OA link when available
- Concepts
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Computer science, Boosting (machine learning), Artificial intelligence, Machine learning, Algorithm, Scope (computer science), Field (mathematics), Drug development, Gradient boosting, Context (archaeology), Drug, Medicine, Mathematics, Biology, Pure mathematics, Random forest, Paleontology, Psychiatry, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 18Per-year citation counts (last 5 years)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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