Immunotherapy has transformed the landscape of cancer treatment, but its toxicities are often lifelong and severe. Furthermore, Immunotherapy related Adverse Events (IrAEs) often necessitate steroid prescription, which makes immunotherapies less effective, and the end of treatment. The biological mechanisms of IrAEs is unknown, and so is the “type” of patient who gets them: currently oncologists have to guess whether a given patient will suffer from an IrAE when on an Immune Checkpoint Inhibitor (ICI). This lack of knowledge about IrAEs is due to a lack of granular, population scale toxicity dataset. My project hypothesizes that the day-to-day experience of cancer patients can be highly informative for biological discovery. I will be using Large Language Models (LLMs) to extract toxicity data from 85,000 patients at Memorial Sloan Kettering Cancer Center, and I will then associate IrAEs with biomarkers through computational analysis. Hopefully, my project will lead to the discovery of a new predictor of IrAEs that Oncologists can test for.