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Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction

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Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why. We test this assumption on five-year Alzheimer's disease and related dementias (ADRD) prediction from longitudinal health histories.

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arXiv Machine Learning
Jul 28

Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement

arXiv:2607. 22770v1 Announce Type: new Abstract: Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex overlapping symptoms among diseases.

By Siyuan Du, Mengxi Chen, Xinyang Jiang, Zilong Wang, Jiangchao Yao, Dongsheng Li, Ya Zhang, Lili Qiu, Yanfeng Wang
arXiv AI
Jul 15

From Critic to Confidence: PPO for Language-Based Quantitative Prediction with Confidence Estimation

arXiv:2607. 12687v1 Announce Type: cross Abstract: LLMs can perform language-based quantitative prediction from unstructured inputs, but remain susceptible to hallucinations and overconfident errors, making it critical to know not only what a model predicts, but when its predictions can be trusted.

By Mehak Dhaliwal, Rasta Tadayon, Andong Hua, Haewon Jeong, Yao Qin