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基于变压器网络的捷克医疗编码助手.
Ladislav Lenc1, Jiří Martínek1, Josef Baloun1
1Dept. of Computer Science & Engineering, Faculty of Applied Sciences, University of West Bohemia, Univerzitni 8, Plzeň, 30100, Czech Republic; NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Univerzitni 8, Plzeň, 30100, Czech Republic.
Computers in biology and medicine
|June 14, 2024
概括
本研究介绍了捷克共和国临床编码人员的AI助理,自动化国际疾病分类 (ICD) 代码预测,以提高医疗报告编码的准确性和效率.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 在捷克共和国,使用国际疾病分类 (ICD) 的医疗报告的手动临床编码容易出现错误,并且由于人为因素而昂贵.
- 现有的流程需要广泛的编码员培训和每份报告的大量努力,导致不准确.
研究的目的:
- 开发和实施一个人工智能驱动的系统,通过自动预测ICD诊断代码来协助临床编码器.
- 通过提交给编码器审查的自动预测来提高医疗报告编码的效率和准确性.
主要方法:
- 开发小型的,以变压器为基础的模型,用于捷克语的ICD分类,重点是最小的内存消耗,通用性,可移植性和可持续性.
- 介绍了一种具有多个分类头的新型"四头"变压器模型,旨在节省内存和学习时间,同时保持性能.
- 对主要和所有诊断分类任务的捷克数据集的模型进行评估,并与使用Mimic IV数据集的最新英语模型进行比较.
主要成果:
- 拟议的基于变压器的模型实现了与单个模型相比的或优于单个模型的结果",四头"模型在内存和训练时间方面提供了显著的节省.
- 开发的模型表现出与最先进的英语模型相提并论的性能,尽管它们的尺寸更小.
- 这些模型被优化为在标准医院计算机系统上快速使用,并允许轻松地与新数据进行重新训练.
结论:
- 开发的AI系统通过自动化ICD代码预测,有效地协助临床编码人员,从而提高医疗报告处理的效率和准确性.
- 拟议的变压器模型,特别是"四头"架构,为捷克临床编码提供了计算高效和有效的解决方案.
- 该研究强调了较小,专业化的模型在资源有限的环境中提供准确和可持续的医疗代码预测的潜力.
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