对合成医疗文本,时间序列和纵向数据的生成性AI模型的审查
Mohammad Loni1, Fatemeh Poursalim2, Mehdi Asadi3
1School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden. mohammad.loni@mdu.se.
NPJ digital medicine
|May 15, 2025
概括
本综述探讨了合成健康记录 (SHR) 的模型. 敌对网络,概率模型和大型语言模型在生成纵向数据,时间序列和医学文本方面都非常出色.
科学领域:
- 数字医学 数字医学
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 合成健康记录 (SHR) 对数字医学研究至关重要.
- 生成现实的SHR在数据模式和隐私方面带来了挑战.
研究的目的:
- 对生成SHRs的实用模型进行范围审查.
- 分析研究目标,数据类型和SHR生成中的方法.
- 识别 SHR 生产技术的研究差距和进展.
主要方法:
- 对52个符合条件的出版物进行系统范围审查.
- 基于SHR数据模式的研究分类:时间序列,纵向和文本.
- 分析研究目标,包括保护隐私,阶级不平衡,数据稀缺和归算.
主要成果:
- 审查了52个出版物:时间序列22个,纵向数据17个,医学文本13个.
- 隐私保护成为了首要的研究目标.
- 基于对抗网络的模型在纵向数据生成方面表现出色.
- 概率模型对于时间序列数据来说是优越的.
- 大型语言模型在医学文本生成方面表现出有效性.
结论:
- 特定模型显示不同合成健康记录数据类型的优越性.
- 在量化SHRs重新识别风险方面存在重大研究差距.
- 需要进一步的研究来开发可靠的SHR隐私性能指标.
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