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作 者:付国华 赵南冰 雷毅 王青[4] 刘凯 高萍 陈适[7] Fu Guohua;Zhao Nanbing;Lei Yi;Wang Qing;Liu Kai;Gao Ping;Chen Shi(Department of Paediatrics,Langfang Maternal and Child Health Hospital,Langfang 065099,Hebei Province,China;Software School of North Central University;Informatics of Software School, Beijing University of Technology;Department of Automation, Tsinghua University;Digital Polytechnology(Beijing)Technology Co., Ltd.;Department of Stomatology, Langfang Maternal and Child Health Hospital;Department of Endocrinology, Peking Union Medical College, Chinese Academy of Medical Sciences, Key Laboratory of Endocrinology, Ministry of Health Laboratory)
机构地区:[1]廊坊市妇幼保健院儿保科,河北廊坊065099 [2]中北大学软件学院 [3]北京工业大学信息学部软件学院 [4]清华大学自动化系 [5]数聚工研(北京)科技有限公司 [6]廊坊市妇幼保健院口腔科 [7]中国医学科学院北京协和医学院内分泌科,卫生部内分泌重点实验室
出 处:《中国数字医学》2022年第11期62-67,共6页China Digital Medicine
基 金:廊坊市科技支撑计划项目(2019013027)。
摘 要:目的:构建矮小症辅助诊断系统,实现儿童生长发育期间矮小症的早发现早治疗目标。方法:统计整理矮小症疾病临床指征和面部特征,基于多模态数据搭建矮小症知识图谱;通过知识图谱进行推理,并将患者数据与相关疾病进行关联,输出指定个数的最相关疾病及信息。结果:该方法在验证集上输出的最相关疾病正确率为80.3%,输出多个结果时可有效缩小疾病范围。结论:基于该方法构建的医疗辅助诊断系统可展示矮小症患者的数据处理过程和疾病诊断结果,为儿童生长发育专科医疗工作者提供参考。Objective To build an auxiliary diagnosis system for short stature and achieve the goal of early detection and treatment during children’s growth and development.Methods The clinical indications and facial features of short stature were statistically collated,and a knowledge graph of short stature was built based on multimodal data.The knowledge graph was used for inference,and the patient datas were correlated with related diseases to output a specific number of most relevant diseases and information.Results The accuracy rate of the most relevant diseases output by the method on the validation set was 80.3%,and the range of diseases could be effectively narrowed when multiple results were output.Conclusion The medical auxiliary diagnosis system constructed based on this method can demonstrate the data processing process and disease diagnosis results for short stature patients,and provide reference for medical practitioners specializing in child growth and development.
分 类 号:TP311[自动化与计算机技术—计算机软件与理论]
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