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深度双向LSTM用于疾病分类,支持基于预诊断的入院:越南的一个案例研究

Hai Thanh Nguyen¹, Khoa Dang Dang Le², Ngoc Huynh Pham³

¹College of Information and Communication Technology, Can Tho University, Can Tho, 900000 Vietnam.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

|June 26, 2023

概括

一个使用双向LSTM的基于文本的新型疾病诊断系统在分类10种疾病时实现了0.982 AUC. 这种方法可以自动化患者流动,提高越南医疗保健的效率.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
自然语言处理自然语言处理.

背景情况:

越南的医院过度拥挤导致患者接待和诊断的重大延迟.
目前的患者流程流程耗时,影响整体医疗保健服务.
需要自动化解决方案来简化医院运营.

研究的目的:

开发和评估基于文本的疾病诊断系统.
利用自然语言处理和机器学习来实现高效的患者预诊断.
评估双向长期短期记忆 (LSTM) 在根据症状对疾病进行分类方面的表现.

主要方法:

使用的文本处理技术:字袋,术语频率-反向文档频率 (TF-IDF) 和标记器.
采用机器学习分类器,包括随机森林 (RF),多层感知器 (MLP) 和嵌入式双向LSTM.
在来自越南医院的230,457个诊断前样本上训练和测试模型.

主要成果:

双向LSTM模型实现了0.982.98的高曲线下面积 (AUC).
通过患者的症状描述,成功地分类了10种不同的疾病.
证明了深度学习模型在分析临床文本数据中的有效性.

结论:

提出的基于文本的疾病诊断系统显示了自动化患者预诊断的巨大潜力.
这种方法可以帮助缓解医院的拥挤情况,并优化患者流动.
未来的实施可以提高越南的医疗保健效率和患者结果.

关键词:

诊断诊断诊断诊断诊断疾病的分类疾病的分类.患者患者患者患者症状症状症状症状

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