[茶饮对胃肠道疾病风险的双重作用:使用整合可解释机器学习和大型语言模型的风险预测模型进行分析]
Junyao Chen1, Zeyu Chen2, Zhaojie Lin1
1School of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, China.
概括
这项研究开发了一个机器学习模型来预测胃肠道疾病风险,发现年龄,DOB和吸烟史是关键因素. 适度饮酒和茶饮,以及戒烟,可能有助于预防这些疾病.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 胃肠道疾病在全球范围内对健康造成重大负担.
- 准确的风险预测对于早期干预和管理至关重要.
- 集成先进的计算模型可以增强诊断能力.
研究的目的:
- 开发和验证胃肠道疾病风险预测模型.
- 探索包括茶饮在内的生活方式因素与胃肠道疾病风险之间的相关性.
- 利用可解释的机器学习和大型语言模型来增强临床决策支持.
主要方法:
- 调查了503名接受胃镜检查和13C尿素呼吸测试的患者队列.
- 训练和评估了多种机器学习模型 (SVM,KNN,LR,RF,XGB,DNN).
- 贝叶斯优化调整了超参数,而夏普利添加式扩展 (SHAP) 提供了模型可解释性.
- 通过使用胃肠道疾病数据和咨询记录,对一个大型语言模型进行了微调.
主要成果:
- 深度神经网络 (DNN) 模型在预测胃肠道疾病风险方面表现优异 (准确率:0.68,回忆:0.85,AUC:0.74).
- 确定的主要预测因素是年龄,出生日期 (DOB) 值和吸烟史.
- 微调的大型语言模型提供了与专家医生的临床建议相当的临床建议.
结论:
- DNN模型为临床风险评估提供了可靠的工具,并有助于内镜手术决策.
- 建议改变生活方式,包括戒烟,适度饮酒和均衡饮用茶,以预防胃肠道疾病.
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