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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Vydáno v:	Clinical and translational science Ročník 18; číslo 3; s. e70172 - n/a
Hlavní autoři:	Wiens, Matthew, Verone‐Boyle, Alissa, Henscheid, Nick, Podichetty, Jagdeep T., Burton, Jackson
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States John Wiley & Sons, Inc 01.03.2025 John Wiley and Sons Inc Wiley
Témata:	Algorithms Artificial Intelligence Artificial Intelligence and Machine Learning boosting Boosting Machine Learning Algorithms Classification Clinical trials Clinical Trials as Topic Datasets Decision trees Drug development Drug Development - methods Flexibility Humans Machine Learning Neural networks Online tutorials quantitative clinical pharmacology Regression analysis Software Tutorial XGBoost quantitative clinical pharmacology boosting machine learning XGBoost
ISSN:	1752-8054, 1752-8062, 1752-8062
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Shrnutí:	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.
Bibliografie:	The authors received no specific funding for this work. Funding Jagdeep Podichetty and Jackson Burton Co‐last author. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Funding: The authors received no specific funding for this work.
ISSN:	1752-8054 1752-8062 1752-8062
DOI:	10.1111/cts.70172