使用密集护理医疗信息中心数据集对国际疾病分类编码模型的自动化编码模型进行系统审查
Ying Zhang1, Chen Lyu2, Lu Chang1
1Department of Medical Record, Guangdong Women and Children Hospital, Guangzhou, China.
国际疾病分类自动化 (ICD) 编码模型显示了进展,特别是在深度学习和知识图表方面. 然而,数据多样性和临床验证等挑战阻碍了实施,需要未来关注多式联运数据和可解释性.
科学领域:
- 计算机科学 计算机科学
- 临床信息学 临床信息学
- 医疗信息学 医疗信息学
背景情况:
- 自动化国际疾病分类 (ICD) 编码对于医疗保健数据管理至关重要.
- 密集护理医疗信息中心 (MIMIC) 数据集是开发这些模型的关键资源.
- 整合计算机科学和临床观点对于推进ICD编码自动化至关重要.
研究的目的:
- 系统地审查自动化ICD编码模型的发展.
- 评估进展,识别挑战,并为未来ICD编码自动化研究提供见解.
- 分析数据预处理,知识整合,模型架构,评估和可解释性的趋势.
主要方法:
- 系统审查遵循系统审查和元分析 (PRISMA) 准则的首选报告项目.
- 选择了2014年至2024年间发表的73项研究.
- 提取有关方法,数据集 (包括MIMIC-III) 和结果的数据.
主要成果:
- 很大一部分研究 (69.57%) 使用了医学知识,主要是知识图.
- 方法的演变从传统的机器学习到深度学习,知识推理和生成模型.
- 自2019年以来,F1-微评分的持续改善,MIMIC-III数据集的显着增加 (6.4%完整,10.2%前50名).
- 在60.27%的研究中实施了可解释性策略,通常涉及注意力可视化.
结论:
- 自动化ICD编码已经进步,但仍存在重大挑战,包括数据多样性和临床编码器验证.
- 医学知识的不充分使用和复杂的算法仍然是模型实施的障碍.
- 未来的研究应该优先考虑多式联运数据集成,增强医学知识应用,以及改进模型可解释性.
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