The study aims to enhance cancer treatment by predicting symptoms from clinical notes using a specialized pre-trained language model, Symptom-BERT. Developed using a clinical dataset from a Midwestern U.S. healthcare system, the model underwent comprehensive stages of pre-training on numerous clinical documents and fine-tuning on annotated notes related to cancer symptoms, further validated with synthetic notes from Chapter-4. The research highlights Symptom-BERT’s efficacy in symptom identification, underscoring the model’s superior performance due to domain-specific pre-training. The conclusion emphasizes the study’s contribution to patient-centered AI, with potential benefits in improving symptom management and patient self-care in the realm of cancer care.