Coles, A.D. orcid.org/0000-0002-2657-0090, McInerney, C.D. orcid.org/0000-0001-7620-7110, Zucker, K. et al. (3 more authors) (2024) Evaluation of machine learning methods for the retrospective detection of ovarian cancer recurrences from chemotherapy data. ESMO Real World Data and Digital Oncology, 4. 100038. ISSN 2949-8201
Abstract
Background Cancer recurrences are poorly recorded within electronic health records around the world. This hinders research into the efficacy of cancer treatments. Currently, the retrospective identification of recurrence/progression diagnosis dates is achieved by staff who manually review patients’ health records. This is expensive, time-consuming, and inefficient. Machine Learning models may expedite the review of health records and facilitate the assessment of alternative cancer therapies. Materials and methods This paper evaluates the use of four machine learning models (random forests, conditional inference trees, decision trees, and logistic regression) in identifying proxy dates of epithelial ovarian cancer recurrence/progression from chemotherapy data, in 531 patients at Leeds Teaching Hospital Trust. Results The random forest achieved the highest F1 score of 0.941 (95% confidence interval 0.916-0.968) when identifying recurrence events. Both the random forest and decision tree models’ classifications closely conform to chart-reviewed time to next treatment, serving as a surrogate for recurrence-free survival. Additionally, all models reached an F1 score >0.940 when identifying patients whose cancer recurred/progressed. Conclusions Our models proficiently identify both proxy dates for recurrence/progression diagnoses and patients whose cancer recurred/progressed. Considering the similar performance of the random forest and decision tree, model preference should be determined by the interpretability required to assist chart review and the ease of implementation into existing architecture.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Published by Elsevier Ltd on behalf of European Society for Medical Oncology. User License: Creative Commons Attribution (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/ |
Keywords: | cancer recurrence; chemotherapy; electronic health record; machine learning; artificial intelligence |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Dec 2024 12:40 |
Last Modified: | 13 Dec 2024 12:40 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.esmorw.2024.100038 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220750 |