从专有打印表格到标准化的数字交换格式 - 护理过渡记录以FHIR为例
Viktor Werlitz1, Lukas Kleybolte2, Sabahudin Balic1
1Institute for agile Software Development, Technical University of Applied Sciences, Augsburg, Augsburg, Germany.
Studies in health technology and informatics
|September 3, 2025
概括
这项研究开发了一种机器学习管道,自动从扫描的护理过渡记录 (CTR) 中提取信息. 该系统以合成数据进行训练,有望实现医疗保健文档的数字化和改进互操作性.
科学领域:
- 医疗信息学
- 计算机科学
- 人工智能
背景情况:
- 将纸质护理过渡记录 (CTR) 数字化为HL7 FHIR等标准化格式, 由于文件变化和采用速度缓慢, 面临挑战.
- 在数字化过程中保护数据完整性和隐私对于医疗机构至关重要.
研究的目的:
- 开发和评估机器学习管道,从扫描的CTR中自动提取信息.
- 评估合成数据用于培训模型的可行性,以克服隐私限制.
主要方法:
- 开发了一个使用Detectron2,LayoutParser和Tesseract OCR的机器学习管道.
- 合成CTR数据用于训练,检查框使用像素密度分析.
- 通过自定义的序列化工具将提取的信息映射到基于FHIR的PIO-ULB格式.
主要成果:
- 该模型在合成数据上实现了高性能指标 (97% F1得分).
- 在现实数据上观察到强的表现,F1得分为85%.
- 现实数据的性能较低与布局变化和扫描文物有关.
结论:
- 机器学习,特别是合成数据,可用于自动化CTR提取和标准化.
- 这种方法为提高医疗保健文档的互操作性提供了潜在的临时解决方案.
- 未来的工作包括扩大合成数据生成和集成先进的识别方法.
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