{"ID":2839273,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17662","arxiv_id":"2511.17662","title":"Enhancing Breast Cancer Prediction with LLM-Inferred Confounders","abstract":"This study enhances breast cancer prediction by using large language models to infer the likelihood of confounding diseases, namely diabetes, obesity, and cardiovascular disease, from routine clinical data. These AI-generated features improved Random Forest model performance, particularly for LLMs like Gemma (3.9%) and Llama (6.4%). The approach shows promise for noninvasive prescreening and clinical integration, supporting improved early detection and shared decision-making in breast cancer diagnosis.","short_abstract":"This study enhances breast cancer prediction by using large language models to infer the likelihood of confounding diseases, namely diabetes, obesity, and cardiovascular disease, from routine clinical data. These AI-generated features improved Random Forest model performance, particularly for LLMs like Gemma (3.9%) and...","url_abs":"https://arxiv.org/abs/2511.17662","url_pdf":"https://arxiv.org/pdf/2511.17662v1","authors":"[\"Debmita Roy\"]","published":"2025-11-20T22:19:31Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.QM\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}