Wójcik, Z., Dimitrova, V. orcid.org/0000-0002-7001-0891, Warrington, L. orcid.org/0000-0002-8389-6134 et al. (2 more authors) (2024) Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Changes During Chemotherapy. In: Finkelstein, J., Moskovitch, R. and Parimbelli, E., (eds.) Artificial Intelligence in Medicine. 22nd International Conference, AIME 2024, 09-12 Jul 2024, Salt Lake City, UT, USA. Lecture Notes in Computer Science, 14844 . Springer Nature , pp. 101-116. ISBN 978-3-031-66538-7
Abstract
Patients undergoing chemotherapy often experience adverse effects, which can lead to changes in health-related quality of life (HRQOL) and have detrimental effects on patients’ physical and psychological wellbeing. This study aims to apply machine learning (ML) models to patient-reported, clinical, and demographic data to predict changes in physical well-being, social functioning, role functioning, usual activities, and mobility at 6, 12 and 18 weeks from starting chemotherapy. A patient-centric approach is followed as outcome variables were selected after consultation with patients and a clinician, who also was involved in the study design. Logistic regression, random forest, extreme gradient boosting, and multilayer perceptron were developed and their performance of predicting improvement and deterioration in HRQOL was evaluated with accuracy, recall, specificity, and area under the ROC curve (AUC). Model performance was generally better when predicting improvement, with best models giving AUC of 0.904 for predicting mobility improvement at 12 weeks and AUC of 0.898 for predicting usual activities improvement at 18 weeks. The results encourage involving stakeholders in research and support the view that ML can be used to predict outcomes meaningful to patients. They also highlight that although some outcome variables can be valuable for patients, they may not be predicted well by ML models. This study can inform future work on patient-centric ML methods contributing to treatment decisions in oncology.
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | © The Authors 2024. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-66538-7_12 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Research (LIMR) > Division of Oncology The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Aug 2024 10:29 |
Last Modified: | 09 Aug 2024 10:29 |
Status: | Published |
Publisher: | Springer Nature |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-031-66538-7_12 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:215823 |
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Filename: AIME 2024 Paper - Final version ID #170.pdf
