通过视觉问题回答数字化医疗表格:我们还在吗?
Christopher Gundler1, Alexander Johannes Wiederhold1, Monika Pötter-Nerger2
1Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf.
Studies in health technology and informatics
|August 8, 2025
概括
多模式神经网络显示出从非结构化医疗文件中提取数据的前景. 然而,目前的准确性低于临床标准,凸显了复杂的医疗记录的挑战.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 医疗保健数据通常存在于非结构化格式中,阻碍了有效的分析和检索.
- 从临床文件中自动提取数据对于提高医疗保健效率和研究至关重要.
研究的目的:
- 评估多式联络神经网络在将非结构化医疗文件转换为结构化数据方面的有效性.
- 评估神经学医疗记录上的高级视觉问题答案模型的性能.
主要方法:
- 策划了来自汉堡-埃潘多夫大学医学中心神经学文件的数据集.
- 采用和评估各种多式联网神经网络模型,包括最近的2024进展.
- 利用视觉问答技术来提取数据.
主要成果:
- 最近的模型表现出更好的性能,但没有达到临床准确性标准.
- 在将这些技术应用于复杂和异质的医疗记录时,发现了重大挑战.
- 目前的模型功能在临床数据提取方面落后于人类水平的性能.
结论:
- 需要进一步开发以实现自动数据提取的临床上可接受的准确性.
- 更大,更多样化的数据集对于训练强大的多式联络神经网络至关重要.
- 临床数据的复杂性需要持续的研究和模型改进.
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