用大模型从临床数据推断并发症风险，提升乳腺癌预测准确率
Enhancing Breast Cancer Prediction with LLM-Inferred Confounders
- 用大模型分析临床数据，推断糖尿病等并发症概率
- 使随机森林模型性能提升，最高达6.4%
- 适合临床早筛与共享决策场景
本研究通过大语言模型从常规临床数据中推断糖尿病、肥胖和心血管疾病等混杂疾病的概率，以增强乳腺癌预测。这些AI生成特征显著提升了随机森林模型的表现，其中Gemma模型提升3.9%，Llama模型提升6.4%。该方法具有非侵入性预筛潜力，适用于临床整合，有助于提升乳腺癌早期发现效率与医患共同决策水平。
原文摘要 · Abstract (English)
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.
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