目的设计并实现以Hadoop分布式存储技术为核心的区域医联体云医疗影像储存系统(Picture Archiving and Communication System,PACS),用以提升医联体框架下医学影像的读取、管理能力,促进优质医学影像资源的共享共用。方法以Hadoop分布...目的设计并实现以Hadoop分布式存储技术为核心的区域医联体云医疗影像储存系统(Picture Archiving and Communication System,PACS),用以提升医联体框架下医学影像的读取、管理能力,促进优质医学影像资源的共享共用。方法以Hadoop分布式存储技术为核心,以Web用户接口、中间接口、HDFS分布式存储、虚拟化容错和虚拟化为系统架构组成,设计并实现区域医联体下的云PACS。结果所设计的系统包含单点登录、患者信息管理、影像读片等功能,满足区域医联体医学影像调阅、管理的工作需求。进一步,通过对比网络文件系统(Network File System,NFS)的文件读取测试结果发现,有着平均2.4倍的速度提升,且两者比较差异有统计学差异(t=82.6,P<0.01)。结论在系统实现方面,该研究描述了系统的核心实现方法、架构、部件及用户界面,具有良好的借鉴意义。在系统应用方面,所设计系统的文件读取速度要明显优于传统NFS的系统,对于医联体的信息化水平提升、医学影像信息的共享共用促进等有着积极作用。展开更多
目的建立以心肌病为研究对象的专病数据库,为心肌病临床研究提供支持。方法以患者主索引(Enterprise Main Patient Index,EMPI)为主线,在临床数据中心的基础上经过数据整合、治理、质控,构建心肌病专病数据库。结果心肌病专病数据库包...目的建立以心肌病为研究对象的专病数据库,为心肌病临床研究提供支持。方法以患者主索引(Enterprise Main Patient Index,EMPI)为主线,在临床数据中心的基础上经过数据整合、治理、质控,构建心肌病专病数据库。结果心肌病专病数据库包含12个模块,下设33个子模块,共收集12023例患者数据,配合完成2项心肌病临床研究。结论专病数据库的建立,增加了医疗数据利用率,为临床研究提供了强有力的信息服务和数据支撑。展开更多
以医院手术工勤人员的全流程智慧追溯为建设场景,以改善射频识别(Radio Frequency Identification,RFID)物联网手术工勤人员定位精度低、鲁棒性差问题为研究目标,提出一种基于卡尔曼滤波和图基平滑的室内定位优化方法,增强定位准确性。...以医院手术工勤人员的全流程智慧追溯为建设场景,以改善射频识别(Radio Frequency Identification,RFID)物联网手术工勤人员定位精度低、鲁棒性差问题为研究目标,提出一种基于卡尔曼滤波和图基平滑的室内定位优化方法,增强定位准确性。该研究首先以图的数据结构对场景进行建模,接着基于卡尔曼滤波(Kalman Filtering)和图基平滑(Tukey Smoothing)算法建立RFID室内定位优化方法,最后通过定位优化模块改善手术工勤人员定位追溯的成效。实证分析结果表明:该方法能有效改善定位精度,方法应用的3个月内手术工勤人员追溯准确率为99.75%。展开更多
Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, whic...Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.展开更多
文摘目的设计并实现以Hadoop分布式存储技术为核心的区域医联体云医疗影像储存系统(Picture Archiving and Communication System,PACS),用以提升医联体框架下医学影像的读取、管理能力,促进优质医学影像资源的共享共用。方法以Hadoop分布式存储技术为核心,以Web用户接口、中间接口、HDFS分布式存储、虚拟化容错和虚拟化为系统架构组成,设计并实现区域医联体下的云PACS。结果所设计的系统包含单点登录、患者信息管理、影像读片等功能,满足区域医联体医学影像调阅、管理的工作需求。进一步,通过对比网络文件系统(Network File System,NFS)的文件读取测试结果发现,有着平均2.4倍的速度提升,且两者比较差异有统计学差异(t=82.6,P<0.01)。结论在系统实现方面,该研究描述了系统的核心实现方法、架构、部件及用户界面,具有良好的借鉴意义。在系统应用方面,所设计系统的文件读取速度要明显优于传统NFS的系统,对于医联体的信息化水平提升、医学影像信息的共享共用促进等有着积极作用。
文摘目的建立以心肌病为研究对象的专病数据库,为心肌病临床研究提供支持。方法以患者主索引(Enterprise Main Patient Index,EMPI)为主线,在临床数据中心的基础上经过数据整合、治理、质控,构建心肌病专病数据库。结果心肌病专病数据库包含12个模块,下设33个子模块,共收集12023例患者数据,配合完成2项心肌病临床研究。结论专病数据库的建立,增加了医疗数据利用率,为临床研究提供了强有力的信息服务和数据支撑。
文摘以医院手术工勤人员的全流程智慧追溯为建设场景,以改善射频识别(Radio Frequency Identification,RFID)物联网手术工勤人员定位精度低、鲁棒性差问题为研究目标,提出一种基于卡尔曼滤波和图基平滑的室内定位优化方法,增强定位准确性。该研究首先以图的数据结构对场景进行建模,接着基于卡尔曼滤波(Kalman Filtering)和图基平滑(Tukey Smoothing)算法建立RFID室内定位优化方法,最后通过定位优化模块改善手术工勤人员定位追溯的成效。实证分析结果表明:该方法能有效改善定位精度,方法应用的3个月内手术工勤人员追溯准确率为99.75%。
基金the Artificial Intelligence Innovation and Development Project of Shanghai Municipal Commission of Economy and Information (No. 2019-RGZN-01081)。
文摘Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.
