Previously submitted to: JMIR Medical Informatics (no longer under consideration since Feb 06, 2022)
Date Submitted: Dec 1, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Clinical oncology textual notes analysis using machine learning and deep learning
ABSTRACT
The textual analysis and classification are important research topics, as advances in this field can foster quality in existing clinical systems. Our research explored experimentally text classification methods applied in non-synthetic oncology clinical notes corpora. The experiments were performed in a dataset with 3,308 medical notes. Experiments evaluated the following machine learning and deep learning classification methods: Multilayer Perceptron Neural network, Logistic Regression, Decision Tree classifier, Random Forest classifier, K-nearest neighbors classifier, and Long-Short Term Memory. An experiment evaluated the influence of the corpora preprocessing step on the results, allowing to identify that the classifier’s mean accuracy was leveraged from 26.1% to 86.7% with the per-clinical-event corpus and 93.9% with the per-patient corpus. The best-performing classifier was the Multilayer Perceptron, which achieved 93.90% accuracy, a Macro F1 score of 93.61%, and a Weighted F1 score of 93.99%.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.