A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications
ABSTRACT 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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Clinical and Translational Science |
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| Online Access: | https://doi.org/10.1111/cts.70172 |
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| author | Matthew Wiens Alissa Verone‐Boyle Nick Henscheid Jagdeep T. Podichetty Jackson Burton |
| author_facet | Matthew Wiens Alissa Verone‐Boyle Nick Henscheid Jagdeep T. Podichetty Jackson Burton |
| author_sort | Matthew Wiens |
| collection | DOAJ |
| description | ABSTRACT 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. |
| format | Article |
| id | doaj-art-7aa5e19ae5d644aab807bed8dc20ede4 |
| institution | OA Journals |
| issn | 1752-8054 1752-8062 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj-art-7aa5e19ae5d644aab807bed8dc20ede42025-08-20T01:49:35ZengWileyClinical and Translational Science1752-80541752-80622025-03-01183n/an/a10.1111/cts.70172A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development ApplicationsMatthew Wiens0Alissa Verone‐Boyle1Nick Henscheid2Jagdeep T. Podichetty3Jackson Burton4Metrum Research Group Boston Massachusetts USABiogen Cambridge Massachusetts USACritical Path Institute Tucson Arizona USACritical Path Institute Tucson Arizona USABiogen Cambridge Massachusetts USAABSTRACT 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.https://doi.org/10.1111/cts.70172boostingmachine learningquantitative clinical pharmacologyXGBoost |
| spellingShingle | Matthew Wiens Alissa Verone‐Boyle Nick Henscheid Jagdeep T. Podichetty Jackson Burton A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications Clinical and Translational Science boosting machine learning quantitative clinical pharmacology XGBoost |
| title | A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications |
| title_full | A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications |
| title_fullStr | A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications |
| title_full_unstemmed | A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications |
| title_short | A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications |
| title_sort | tutorial and use case example of the extreme gradient boosting xgboost artificial intelligence algorithm for drug development applications |
| topic | boosting machine learning quantitative clinical pharmacology XGBoost |
| url | https://doi.org/10.1111/cts.70172 |
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