Enhancing clinical decision-making in radiation therapy: a natural language processing and machine learning approach

Radiation oncology is a specialized medical field that employs various forms of radiation therapy to treat cancer and other diseases. Decision-making in this domain often involves intricate considerations. This paper investigates applying natural language processing (NLP) techniques for classifying clinical text data to recommend the appropriate radiation therapy modality proton therapy or photon therapy. We use the Clinical Text Classification subset of the ROND database, which consists of only 100 manually annotated cancer cases. We start by pre-processing the text data, extracting TF-IDF features, and applying random oversampling to handle the class imbalance issue. We train and evaluate several machine learning models, including support vector machines (SVM), random forest, decision trees, and naive Bayes on this textual data alone. The SVM model achieved the highest accuracy of 95% after oversampling. To enhance interpretability, we apply SHAP values method which provide insights into which features are most important for the model's predictions towards recommending therapy and how they impact those predictions, which be crucial for understanding and interpreting machine learning model.