从入院生成患者实验室测试订单的新型模型:变压器模型方法
Yuki Hyohdoh1, Kazukuni Yoshua Nomura1, Keita Mitani1
1Center of Medical Information Science, Kochi Medical School, Kochi University.
Studies in health technology and informatics
|August 8, 2025
概括
本研究介绍了一种GPT-2模型,用于生成合成患者实验室测试订单数据,保护隐私并改善医疗保健中的序列预测. 该模型有效地使用时间数据,在患者入院后进行更准确的预测.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 对具有统计实用性的隐私保护医疗伪数据的需求日益增加.
- 在医疗保健环境中,对先进的序列预测模型的需求越来越大.
- 在处理患者数据的时间动态方面,现有方法的局限性.
研究的目的:
- 开发和评估基于GPT-2的模型,用于生成患者实验室测试订单序列.
- 将时间过程纳入合成医疗数据生成中.
- 评估模型的性能与传统机器学习方法相比.
主要方法:
- 利用了来自日本高知医学院医院的患者数据.
- 将患者个人资料和订单数据转换为分类格式.
- 雇佣了SentencePiece的代币化,并训练了一个GPT-2变压器模型.
- 使用F分数评估模型性能,与渐变增强决策树 (GBDT) 模型进行比较.
主要成果:
- GPT-2模型在预测实验室测试订单方面表现出卓越的表现,当将时间序列信息纳入后入院时.
- 虽然GBDT在初始入院数据方面表现出色,但GPT-2在不断变化的患者数据方面表现出优势.
- 拟议的模型成功生成了实验室测试订单数据,考虑了患者的时间轨迹.
结论:
- GPT-2是一种自然语言处理变压器模型,可以有效地适应生成保护隐私,统计学上有用的医学伪数据.
- 整合时间动态显著提高了对实验室测试订单的序列预测的准确性.
- 开发的模型为医疗保健应用提供了一个有希望的方法,需要随着时间的推移对患者数据进行预测建模.
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