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.
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Acknowledgements
The authors thank the patients and clinicians participating in the eRAPID clinical trial for providing the data; Patient Centred Outcomes Research (PCOR) Group in the University of Leeds, Faculty of Medicine and Health, Leeds Institute of Medical Research for consultations of initial study design; and patient representatives of the Use My Data for discussing research relevance. This work was supported in part by UK Research and Innovation (UKRI) [CDT grant number EP/S024336/1].
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Wójcik, Z., Dimitrova, V., Warrington, L., Velikova, G., Absolom, K. (2024). Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Changes During Chemotherapy. In: Finkelstein, J., Moskovitch, R., Parimbelli, E. (eds) Artificial Intelligence in Medicine. AIME 2024. Lecture Notes in Computer Science(), vol 14844. Springer, Cham. https://doi.org/10.1007/978-3-031-66538-7_12
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