Wojcik, Z. orcid.org/0009-0007-6214-7736, Dimitrova, V., Warrington, L. et al. (3 more authors) (2025) Machine Learning Models Predicting Hospital Admissions During Chemotherapy Utilising Longitudinal Symptom Severity Reports and Patient-Reported Outcome Measures. In: Andrikopoulou, E., Gallos, P., Arvanitis, T.N., Austin, R., Benis, A., Cornet, R., Chatzistergos, P., Dejaco, A., Dusseljee-Peute, L., Mohasseb, A., Natsiavas, P., Nakkas, H. and Scott, P., (eds.) Intelligent Health Systems – From Technology to Data and Knowledge. Medical Informatics Europe, 19-21 May 2025, Glasgow, Scotland. Studies in Health Technology and Informatics, 327 . IOS Press , pp. 178-182. ISBN 978-1-64368-596-0
Abstract
Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients’ well-being. Machine learning (ML) models identifying patients at risk of emergency admissions are often developed on data lacking patients’ perspective. This study used longitudinally collected symptom severity reports and 4 ML models to predict hospital admissions risk during chemotherapy, and short-term admissions risk (within 14 days of a report). It also compared performance of models developed with, and without the use of patient-reported outcome measures (PROMs). Random forest and extreme gradient boosting models predicted admissions with excellent balanced accuracy, recall, and specificity of over 0.9. However, short-term admissions risk predictions were poor. PROMs improved overall model performance. The results advocate for longitudinal collection and use of symptom severity reports and PROMs. This can support understanding of chemotherapy toxicity patterns leading to emergency admissions, and inform clinicians and patients of potential future complications
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). |
Keywords: | Patient-reported data; Hospital admissions predictions; ML |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 May 2025 16:37 |
Last Modified: | 29 May 2025 16:37 |
Status: | Published |
Publisher: | IOS Press |
Series Name: | Studies in Health Technology and Informatics |
Identification Number: | 10.3233/SHTI250297 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:227012 |