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Court Judgment Decision Support System Based on Medical Text Mining

机译：基于医学文本挖掘的法院判决决策支持系统

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摘要

Medical damage is a common problem faced by hospitals around the world and is widely watched by countries and the World Health Organization.As the number of medical damage dispute lawsuit cases rapidly grows, many countries in the world face the problem how to improve the efficiency of the judicial system under the premise of guaranteeing the quality of the trial.Therefore, in addition to reforming the system, the decision support system will effectively improve judicial decisions.This paper takes medical damage judgment documents in China as example, and proposes a court judgment decision support system (CJ-DSS) based on medical text mining and the automatic classification technology.The system can predict the trail results of the new lawsuit documents according to the previous cases verdict-rejected and non-rejected.Combined with the cases, the study in this paper found that combined feature extraction method does improve the performance of three kinds of classifiers-Support Value Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN), the degree of improved performance is different from using DF-CHI combined feature extraction method.In addition, integrated learning algorithm also improves the classification performance of the overall system.
机译：医疗损害是世界各地医院面临的普遍问题，受到各国和世界卫生组织的广泛关注。随着医疗损害纠纷诉讼案件数量的快速增长，世界上许多国家都面临着如何提高医疗损害效率的问题。因此，除了改革该制度外，决策支持系统还将有效地改善司法判决。本文以我国医疗损害判决书为例，提出了法院判决的建议。基于医学文本挖掘和自动分类技术的决策支持系统（CJ-DSS），该系统可以根据以前的判决被拒绝和未拒绝的案例来预测新诉讼文件的追踪结果。本文的研究发现，组合特征提取方法确实可以提高三种分类器的性能。 Chien（SVM），人工神经网络（ANN）和K最近邻（KNN），其改进程度与使用DF-CHI组合特征提取方法有所不同。此外，集成学习算法还提高了分类的性能。整体系统。

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