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首页> 外文会议>Pacific Rim international conference on artificial intelligence >Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes
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Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes

机译：从临床笔记中使用有区别的知识指导医学概念配对来确定冠状动脉疾病的风险

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摘要

Document classification (DC) is one of the broadly investigated natural language processing tasks. Medical document classification can support doctors in making decision and improve medical services. Since the data in document classification often appear in raw form such as medical discharge notes, extracting meaningful information to use as features is a challenging task. There are many specialized words and expressions in medical documents which make them more challenging to analyze. The classification accuracy of available methods in medical field is not good enough. This work aims to improve the quality of the input feature sets to increase the accuracy. A new three-stage approach is proposed. In the first stage, the Unified Medical Language System (UMLS) which is a medical-specific dictionary is used to extract the meaningful phrases by considering disease or symptom concepts. In the second stage, all the possible pairs of the extracted concepts are created as new features. In the third stage, Particle Swarm Optimisation (PSO) is employed to select features from the extracted and constructed features in the previous stages. The experimental results show that the proposed three-stage method achieved substantial improvement over the existing medical DC approaches.
机译：文档分类（DC）是广泛研究的自然语言处理任务之一。医疗文件分类可以帮助医生做出决定并改善医疗服务。由于文档分类中的数据通常以原始形式出现，例如医疗出诊单，因此提取有意义的信息以用作功能部件是一项艰巨的任务。医疗文档中有许多专门的单词和表达方式，使它们的分析更具挑战性。医学领域中可用方法的分类准确性不够好。这项工作旨在提高输入功能集的质量，以提高准确性。提出了一种新的三阶段方法。在第一阶段，统一医学语言系统（UMLS）是一种医学专用词典，用于通过考虑疾病或症状概念来提取有意义的短语。在第二阶段，将所有可能的提取概念对创建为新特征。在第三阶段，使用粒子群优化（PSO）从先前阶段中提取和构造的特征中选择特征。实验结果表明，所提出的三阶段方法与现有的医学DC方法相比有了实质性的改进。

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