一个双流深度模型用于在重症监护病房的自动化ICD-9代码预测
Mustafa Arda Ayden1, Mehmet Eren Yuksel2, Seniha Esen Yuksel Erdem1
1Department of Electrical and Electronics Engineering, Hacettepe University, Ankara, 06800, Türkiye.
Heliyon
|February 20, 2024
概括
这项研究引入了一种新的深度学习框架,用于自动预测国际疾病分类第九版 (ICD-9) 代码. 该模型有效地利用多模式患者数据来提高医疗保健中医疗编码的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 计算医学是一种计算医学.
背景情况:
- 准确的患者医疗编码对于医疗保健业务至关重要,包括计费,记录和程序分析.
- 由于大量的代码,手动分配国际疾病分类第九次修订 (ICD-9) 代码是劳动密集型的.
研究的目的:
- 开发和评估用于预测ICD-9代码的自动化系统.
- 利用多模式数据 (数字和基于文本的) 提高预测准确性.
主要方法:
- 一个双流深度学习框架被设计用于处理多模式的患者数据.
- 对于文本和数字数据,分别使用了文本表示模型和门式循环单位 (GRU).
- 该框架应用于密集护理单位 (ICU) 代码预测的MIMIC-III数据集.
主要成果:
- 拟议的双流模型与现有最先进的方法相比,表现出优越的性能.
- 多模式数据的整合显著提高了ICD-9代码预测准确度.
结论:
- 开发的深度学习框架为自动化ICD-9代码预测提供了有效的解决方案.
- 这种方法可以减少医疗专家的手工负担,并提高医疗数据管理的效率.
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