物理检查的自动提取 总体实践中的交互 咨询:使用图像和文本基础方法的多模式
Moomna Waheed1, Hao Xiong1, Annie Y S Lau1
1Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Studies in health technology and informatics
|September 25, 2024
概括
这项研究开发了自动化方法来识别在人身初级保健访问期间的体检. 文本和图像分析成功地确定了全科医生咨询的这些关键组成部分.
科学领域:
- 医疗信息学 医疗信息学
- 计算机视觉 计算机视觉
- 初级保健研究研究初级保健研究
背景情况:
- 在COVID-19大流行期间,远程咨询的采用激增,在初级保健机构的不断发展.
- 虚拟体检仍然是全面实施远程医疗的重要障碍.
- 准确识别体检组件对于推进远程医疗保健能力至关重要.
研究的目的:
- 开发和评估自动化方法,用于在面对面的全科医生 (GP) 咨询期间识别体检.
- 利用文本和图像数据来增强体检相互作用的识别.
- 通过对身体检查组件进行客观评估,解决当前远程医疗的局限性.
主要方法:
- 基于文本的识别使用了咨询成绩单上的正则表达式来识别与体检相关的关键词.
- 基于图像的识别利用转移学习,特别是微调Resnet-18模型,从视觉数据中检测物理检查事件.
- 研究了一种结合文本和图像分析的混合方法,以提高识别精度.
主要成果:
- 基于文本的方法通过对成绩单的关键词分析来确定体检的有效性.
- 基于图像的方法,使用微调的Resnet-18模型,成功检测到体检相互作用.
- 这两种方法的结合显示出在初级保健中强大的自动识别身体检查的潜力.
结论:
- 使用基于文本和图像的方法对体检的自动识别在初级保健机构中是可行的.
- 这些技术可以提供客观数据来支持远程医疗发展和临床工作流分析.
- 进一步的研究可以完善这些方法,以提高虚拟体检的范围和准确性.
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