Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Pelvic schwannoma misdiagnosed as colorectal cancer metastasis and the corresponding diagnostic lessons: A report of two cases.

Oncology letters·2026
Same author

Debiased medication recommendation through fusing frequent pattern and temporal medical records.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Synthesis of one-dimensional silver nanowires in aqueous solution using phloroglucinol as a reducing reagent.

Dalton transactions (Cambridge, England : 2003)·2026
Same author

Transcriptomic and single-cell analyses reveal the prognostic value of ETV4 and its role in shaping the immune landscape of colorectal cancer.

Translational oncology·2026
Same author

Predicting the Postoperative Recurrence Risk in Soft-Tissue Sarcomas of the Extremities and Trunk Using MRI-Based Nomogram.

Academic radiology·2026
Same author

Microelectric field-enhanced air-lift A/O process: Mechanisms for advanced nutrient removal in low C/N rural domestic sewage.

Journal of environmental management·2026
Same journal

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Artificial intelligence in medicine·2026
Same journal

R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform.

Artificial intelligence in medicine·2026
Same journal

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Artificial intelligence in medicine·2026
Same journal

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Artificial intelligence in medicine·2026
Same journal

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Artificial intelligence in medicine·2026
Same journal

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

Artificial intelligence in medicine·2026
查看所有相关文章

相关实验视频

Updated: Sep 15, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

881

对于自动ICD编码的深度学习:审查,机会和挑战.

Xiaobo Li1, Yijia Zhang1, Xiaodi Hou2

  • 1School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, 116026, China.

Artificial intelligence in medicine
|July 15, 2025
PubMed
概括
此摘要是机器生成的。

深度学习通过克服电子健康记录 (EHR) 的挑战,显著提高了国际疾病分类 (ICD) 的自动编码. 先进的神经网络和辅助数据提高了医疗编码任务的准确性和效率.

关键词:
自动ICD编码自动化ICD编码深度学习是一种深度学习.电子健康记录电子健康记录医疗代码分配 医疗代码分配

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.7K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K

相关实验视频

Last Updated: Sep 15, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

881
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.7K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K

科学领域:

  • 计算机科学 计算机科学
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 自动国际疾病分类 (ICD) 编码对于医疗保健数据管理至关重要,但在复杂的电子健康记录 (EHR) 方面面临挑战.
  • 手动编码是低效和易出错的,而传统的机器学习方法与临床文本语义斗争.
  • 深度学习 (DL) 在解决这些限制方面显示出有前途的希望,用于准确的医疗代码分配.

研究的目的:

  • 综合审查自动ICD编码的深度学习的最新进展.
  • 确定基于DL的ICD编码中的突出挑战和新兴发展趋势.
  • 根据其年份,设计,神经网络和辅助数据分析模型.

主要方法:

  • 基于深度学习的自动ICD编码方法的系统文献综述.
  • 选了5个主要的在线数据库 (科学网络,SpringerLink,PubMed,ACM,IEEE).
  • 收集和分析了2017年至2023年间发表的53篇相关文章.

主要成果:

  • 像CNN,RNN,注意力机制,变压器和PLM这样的深度神经网络可以有效地处理漫长,杂的临床文本和复杂的代码关系.
  • 新兴趋势包括整合医疗本体学 (代码描述,层次结构) 和外部知识 (维基百科,CCS) 以改进编码.
  • 这些方法解决了医疗编码中的高维度和长尾标签分布等挑战.

结论:

  • 深度学习,特别是神经网络和注意力机制,已经在自动ICD编码任务中取得了成功.
  • 整合辅助医疗数据可以提高模型性能和特征表示.
  • 基于深度学习的自动ICD编码在医疗保健中具有显著的未来潜力,目前正在面临的挑战和未来的方向已确定.