To solve the problem of inaccurate entity extraction caused by low application efficiency and big data noise in telemedicine sensing data, a deep learning-based method for entity relationship extraction in telemedicine big data is proposed. By analyzing the distribution structure of the medical sensing big data, the fuzzy function of the distribution shape is calculated and the seed relationship set is transformed by the inverse Shearlet transform. Combined with the deep learning technology, the GMM-GAN data enhancement model is built, the interactive medical sensing big data features are obtained, the association rules are matched one by one, the noiseless medical sensing data are extracted in time sequence, the feature items with the highest similarity are obtained and used as the constraint to complete the feature entity relationship extraction of the medical sensing data. The experimental results show that the extracted similarity of entity relations is more than 70%, which can handle overly long and complex sentences in telemedicine information text; the extraction time is the shortest and the volatility is low.

针对远程医疗感知数据中应用效率低和大数据噪声导致实体提取不准确的问题，提出一种基于深度学习的远程医疗大数据实体关系提取方法。通过分析医学传感大数据的分布结构，计算分布形状的模糊函数，并通过逆Shearlet变换对种子关系集进行变换。结合深度学习技术，构建GMM-GAN数据增强模型，获取交互式医疗传感大数据特征，关联规则一一匹配，按时间顺序提取无噪声医疗传感数据，获取相似度最高的特征项，作为约束条件，完成医学传感数据的特征实体关系提取。实验结果表明，提取的实体关系相似度达到70%以上，可以处理远程医疗信息文本中过长复杂的句子；萃取时间最短，挥发性低。