AI RESEARCH
OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction
arXiv CS.LG
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ArXi:2510.17532v2 Announce Type: replace-cross Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data. While large language models (LLMs) have shown strong performance in biomedical NLP, they often lack structured reasoning capabilities critical for high-stakes decision. We present a unified, multi-task learning framework that aligns autoregressive LLMs with clinical reasoning for outcome prediction on the MSK-CHORD dataset.