Clinical Oncology Textual Notes Analysis Using Machine Learning and Deep Learning

• Diego Pinheiro da Silva UNISINOS https://orcid.org/0000-0003-2106-644X
• William da Rosa Fröhlich PUCRS https://orcid.org/0000-0003-3551-2623
• Marco Antonio Schwertner UNISINOS
• Sandro José Rigo UNISINOS https://orcid.org/0000-0001-8140-5621

Resumo

Advances in textual classification 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 us 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%.