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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Bibliographic Details
Published in:Clinical and translational science Vol. 18; no. 3; pp. e70172 - n/a
Main Authors: Wiens, Matthew, Verone‐Boyle, Alissa, Henscheid, Nick, Podichetty, Jagdeep T., Burton, Jackson
Format: Journal Article
Language:English
Published: United States John Wiley & Sons, Inc 01.03.2025
John Wiley and Sons Inc
Wiley
Subjects:
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
Online Access:Get full text
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Summary: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.
Bibliography:The authors received no specific funding for this work.
Funding
Jagdeep Podichetty and Jackson Burton Co‐last author.
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Funding: The authors received no specific funding for this work.
ISSN:1752-8054
1752-8062
1752-8062
DOI:10.1111/cts.70172