基于CNN-SVM的护理不良事件文本分类研究

针对当前医院护理不良事件上报的内容多为非结构化文本数据，缺乏合理明确的分类，人工分析难度大、人为因素多、存在漏报瞒报、人为降低事件级别等问题，提出一种基于字符卷积神经网络CNN与支持向量机SVM的中文护理不良事件文本分类模型。该模型通过构建字符级文本词汇表对文本进行向量化，利用CNN对文本进行抽象的特征提取，并用SVM分类器实现中文文本分类。与传统基于TF-IDF的SVM、随机森林等多组分类模型进行对比实验，来验证该模型在中文护理不良事件文本分类中的分类效果。

Text classification of nursing adverse events based on CNN-SVM

features of the text,and SVM classifier is used to realize the Chinese text classification.Comparative experiments show that the proposed model has better classification effect than the traditional classification models such as SVM and random forest based on TF-IDF(Term Frequency-Inverse Document Frequency)in the text classification of Chinese nursing adverse events.