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Identification of small cell lung cancer patients who are at risk of developing common serious adverse event groups with machine learning
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Wanika, Linda, Evans, Neil D. and Chappell, Michael J. (2023) Identification of small cell lung cancer patients who are at risk of developing common serious adverse event groups with machine learning. Frontiers in Drug Safety and Regulation, 3 . p. 1267623. doi:10.3389/fdsfr.2023.1267623 ISSN 2674-0869.
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Official URL: https://doi.org/10.3389/fdsfr.2023.1267623
Abstract
Introduction: Across multiple studies, the most common serious adverse event groups that Small Cell Lung Cancer (SCLC) patients experience, whilst undergoing chemotherapy treatment, are: Blood and Lymphatic Disorders, Infections and Infestations together with Metabolism and Nutrition Disorders. The majority of the research that investigates the relationship between adverse events and SCLC patients, focuses on specific adverse events such as neutropenia and thrombocytopenia.
Aim: This study aims to utilise machine learning in order to identify those patients who are at risk of developing common serious adverse event groups, as well as their specific adverse event classification grade.
Methods: Data from five clinical trial studies were analysed and 12 analysis groups were formed based on the serious adverse event group and grade.
Results: The best test runs for each of the models were able to produce an area under the curve (AUC) score of at least 0.714. The best model was the Blood and Lymphatic Disorder group, SAE grade 0 vs. grade 3 (best AUC = 1, sensitivity rate = 0.84, specificity rate = 0.96).
Conclusion: The top features that contributed to this prediction were total bilirubin, alkaline phosphatase, and age. Future work should investigate the relationship between these features and common SAE groups.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > Q Science (General) R Medicine > RC Internal medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Small cell lung cancer , Diagnostic imaging -- Data processing, Small cell lung cancer -- Patients, Small cell lung cancer -- Diagnosis -- Data Processing, Machine learning | ||||||
Journal or Publication Title: | Frontiers in Drug Safety and Regulation | ||||||
Publisher: | Frontiers Media S.A. | ||||||
ISSN: | 2674-0869 | ||||||
Official Date: | 15 September 2023 | ||||||
Dates: |
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Volume: | 3 | ||||||
Page Range: | p. 1267623 | ||||||
DOI: | 10.3389/fdsfr.2023.1267623 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Copyright Holders: | © 2023 Wanika, Evans and Chappell | ||||||
Date of first compliant deposit: | 20 November 2023 | ||||||
Date of first compliant Open Access: | 20 November 2023 | ||||||
RIOXX Funder/Project Grant: |
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