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数据分析与知识发现  2019, Vol. 3 Issue (6): 99-108
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中南财经政法大学信息与安全工程学院 武汉 430073
Deep Neural Network Learning for Medical Triage
Kan Liu(),Lu Chen
School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
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目的】基于患者的主诉和病史, 利用深度学习模型辅助解决医院预检分诊中准确分配科室问题。【方法】将卷积神经网络模型应用于医疗领域的短文本分类问题, 从非结构化的患者主诉文本中学习医疗术语之间的相关性, 进行预训练, 然后将预训练模型的结构、参数及其权重用于含主诉和病史文本的网络中进行调整, 从而得到最终的学习结果。【结果】该算法在数据集上F值达到88%, 超过当前最好的基线模型6个百分点, 同时发现在数据集内容和样本量大小相似的情况下, 使用预训练模型能够明显提高模型训练效率。【局限】本研究基于电子病历数据展开, 与直接使用分诊台患者自述数据相比可能存在差异, 对此有待进一步探讨。【结论】提出的分诊模型有较强分类能力, 能够辅助提高医疗分诊的效率, 推动精准医疗发展。

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关键词 医疗分诊电子病历卷积神经网络预训练    

[Objective] This paper proposes an algorithm to accurately assign specialists for outpatients based on their major complaints and medical histories. [Methods] We applied the convolutional neural network model to classify the medical short texts, and learn the correlation between medical terms, which were the tasks for pre-training. Then, we examined the structure, parameters and weights of the pre-trained model with actual texts of main complaint and medical history. Finally, we modified the network to obtain the final learning outcome. [Results] The F-score of the proposed approach reached 88% with the sample dataset, which was 6% higher than that of the current best baseline model. The pre-trained model significantly improved the training efficiency. [Limitations] We did not directly work with the patient’s actual complaints at the triage desk. We only used their electronic medical records, which might yield inaccurate results. [Conclusions] The proposed triage model improves the efficiency of medical triage, and promote precision medical treatment for patients.

Key wordsMedical Triage    Electronic Medical Records    Convolutional Neural Network    Pre-training
收稿日期: 2018-07-25      出版日期: 2019-08-15
基金资助:*本文系国家自然科学基金项目“基于个性化知识地图的交互式信息检索系统研究”(项目编号: 71573196)的研究成果之一
刘勘,陈露. 面向医疗分诊的深度神经网络学习*[J]. 数据分析与知识发现, 2019, 3(6): 99-108.
Kan Liu,Lu Chen. Deep Neural Network Learning for Medical Triage. Data Analysis and Knowledge Discovery, 2019, 3(6): 99-108.
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