对于对结直肠癌护理的营养影响的计算洞察力,NLP是NLP
Shengnan Gong1, Xiaohong Jin2, Yujie Guo3
1The Affiliated Hospital of Nantong University, Nantong University, Nantong, Jiangsu 226001, PR China.
SLAS technology
|April 19, 2025
概括
一个新的框架使用自然语言处理 (NLP) 和自适应衣群优化大语言模型 (ATSO-LLMs) 来基于饮食预测结直肠癌 (CRC) 风险. 这种方法提高了不同人群的准确性,有助于个性化营养干预.
科学领域:
- 计算生物学是一种计算生物学.
- 营养科学 营养科学
- 在瘤学瘤学.
背景情况:
- 结肠直肠癌 (CRC) 发病率在年轻人中正在增加.
- 现有的CRC预测模型往往缺乏多样性,并未充分利用营养数据.
- 对饮食在CRC中的作用的计算分析是不发达的.
研究的目的:
- 引入对CRC营养影响预测框架 (NICRP框架).
- 为CRC预测利用自然语言处理 (NLP) 和自适应化群集优化大语言模型 (ATSO-LLMs).
- 分析饮食对不同人群中CRC的影响.
主要方法:
- 收集了多样化的结直肠癌饮食和生活方式数据集 (>1000名参与者).
- 使用NLP技术处理的非结构化饮食数据 (删除停止词,小写,句号删除).
- 使用随机过量抽样和应用ATSO-LLM进行分析和预测,重新平衡不平衡的数据.
主要成果:
- 该NICRP框架实现了高预测准确度 (98.4%),灵敏度 (97.6%),特异性 (96.9%) 和F1-Score (96.2%).
- 与ATSO-LLM相结合的NLP衍生功能显著提高了预测性能.
- 观察到最小的错误分类率,表明模型性能强.
结论:
- 该NICRP框架提供了一种新的,数据驱动的方法来理解CRC的营养决定因素.
- 这一进步使医疗保健专业人员能够为各种人群提供精确的预测工具.
- 该框架为改善CRC管理和预防提供了量身定制的饮食干预措施.
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