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相关概念视频

Methods of Documentation VI: Case Management Model01:15

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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Nursing Clinical Information System (NCIS)
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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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相关实验视频

Updated: Jun 4, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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优化人类人工智能的范式 协作临床编码

Yue Gao1,2,3, Yuepeng Chen1,2, Minghao Wang1,2

  • 1School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.

NPJ digital medicine
|December 20, 2024
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此摘要是机器生成的。

这项研究介绍了CliniCoCo,一个用于自动化临床编码 (ACC) 的循环中人框架. 它通过将人类编码器与深度学习相结合来提高效率,减少编码时间并提高电子医疗记录的准确性.

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 改善临床文档 改善临床文档

背景情况:

  • 手动临床编码耗时且容易出现错误.
  • 自动化临床编码 (ACC) 提供了一个潜在的解决方案,但需要人类监督才能在现实世界中应用.
  • 将人类专业知识与ACC系统集成至关重要,以最大限度地提高效率和准确性.

研究的目的:

  • 提出和评估CliniCoCo,这是一种新的HITL框架,用于ACC系统和人类编码器之间的有效协作.
  • 使用深度学习优化注释工作负载并提高编码过程的效率.
  • 评估CliniCoCo对编码时间,准确性和专业编码人员绩效的影响.

主要方法:

  • 开发了一个名为CliniCoCo的Human-in-the-loop (HITL) 框架,利用深度学习实现自动化临床编码 (ACC).
  • 在框架内的注释,培训和用户交互阶段实施了协作策略.
  • 使用来自中国医院的现实世界电子病历 (EMR) 数据集进行实验.

主要成果:

  • 通过优化注释工作负载,CliniCoCo获得了0.80-0.84的F1分数.
  • 该系统证明了能够将30%错误的代码的EMR的注释要求减半,最小的0.01F1减少.
  • 人类评估显示,CliniCoCo减少了40%的编码时间,并提高了EMR错误的校正率,包括在识别缺失代码方面提高了三倍.
  • 专业程序员的表现有了显著的改善,F1的分数从0.72.93提升到0.93以上.

结论:

  • CliniCoCo框架有效地将自动化临床编码与人类专业知识相结合,提高EMR处理的效率和准确性.
  • 与CliniCoCo一样,HITL方法对于优化实际医疗保健环境中的ACC系统至关重要.
  • CliniCoCo在临床编码方面取得了重大进展,减少了工作量并提高了数据质量,以获得更好的医疗保健结果.