人工智能蒸技术的实证评估,以从电子健康记录中确定癌症的结果
Irbaz Bin Riaz1,2, Syed Arsalan Ahmed Naqvi3, Noman Ashraf4
1Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. riaz.irbaz@mayo.edu.
NPJ digital medicine
|June 10, 2025
概括
使用深度学习从电子健康记录 (EHR) 中提取癌症结果具有挑战性. 一个教师-学生框架有效地从癌症研究模型中转移知识,但需要域内数据才能实现最佳性能.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 从非结构化的电子健康记录 (EHR) 中提取癌症研究的表型信息是劳动密集的.
- 深度学习模型提供自动化,但由于数据隐私问题而面临可扩展性挑战.
研究的目的:
- 评估一个教师-学生框架,从电子健康记录中提取纵向临床结果.
- 通过自由文本放射学报告确定癌症结果,特别是使用固体瘤响应评估标准 (RECIST) 的整体反应和进展.
主要方法:
- 阶层变压器架构教师模型在Dana-Farber癌症研究所 (DFCI) 的数据上接受了培训.
- 教师模型标记公共数据集 (MIMIC-IV,Wiki-text) 和合成数据用于学生模型培训.
- 学生模型被训练来复制教师模型预测的知识蒸.
主要成果:
- DFCI教师模型在提取癌症结果方面表现出很高的表现.
- 在MIMIC-IV数据上训练的学生模型取得了与教师模型相似的结果,表明知识转移取得了成功.
- 在Wiki-text和合成数据上训练的学生模型表现不那么有效.
结论:
- 教师-学生框架使有效的知识转移能够从电子健康记录中提取临床结果.
- 学生模型的性能高度依赖于培训数据的领域相关性.
- 在临床应用中,开发域内公共数据集对于成功的模型蒸至关重要.
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