目的分析2018年肝病专科的住院医疗服务能力,为科室精细化管理、提高肝病诊疗能力和加强肝病专科的发展提供数据支持。方法以宁波市某三甲医院肝病专科6个临床科室为研究对象,根据医院2018年住院病案首页数据经分组器分组后的结果,计算...目的分析2018年肝病专科的住院医疗服务能力,为科室精细化管理、提高肝病诊疗能力和加强肝病专科的发展提供数据支持。方法以宁波市某三甲医院肝病专科6个临床科室为研究对象,根据医院2018年住院病案首页数据经分组器分组后的结果,计算各临床科室疾病诊断相关组(Diagnosis Related Groups,DRGs)组数、DRGs总权重数、病例组合指数(Case Mix Index,CMI)、每床位工作负荷、每医师工作负荷、时间消耗指数、费用消耗指数指标,分析各临床科室病种结构,比较各临床科室的病种顺位前10的DRG组的专科时间效率指数和费用效率指数。结果2018年肝病专科6个临床科室中E科DRGs组数最多,CMI值最大,每床位产出最高;F科治疗同类疾病所花费的时间和费用最低,DRGs总权重数和每医师产出最高。不同临床科室主要病种不同。病种顺位前10的各病种时间效率指数最低的科室依次为:D、E、F、C、F、C、E、A、A、E;各病种费用效率指数最低的科室依次为:D、F、F、A、F、C、E、C、A、A。结论DRGs可以对临床专科各临床科室的住院医疗服务能力进行对比分析。展开更多
Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the d...Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM(IFCM) as well as support vector machines(SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65% and a mean positive predictive value of 97.25%. With an image-based criterion, our approach reached a 100% mean sensitivity, 96.43% mean specificity and 98.21% mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.展开更多
文摘目的分析2018年肝病专科的住院医疗服务能力,为科室精细化管理、提高肝病诊疗能力和加强肝病专科的发展提供数据支持。方法以宁波市某三甲医院肝病专科6个临床科室为研究对象,根据医院2018年住院病案首页数据经分组器分组后的结果,计算各临床科室疾病诊断相关组(Diagnosis Related Groups,DRGs)组数、DRGs总权重数、病例组合指数(Case Mix Index,CMI)、每床位工作负荷、每医师工作负荷、时间消耗指数、费用消耗指数指标,分析各临床科室病种结构,比较各临床科室的病种顺位前10的DRG组的专科时间效率指数和费用效率指数。结果2018年肝病专科6个临床科室中E科DRGs组数最多,CMI值最大,每床位产出最高;F科治疗同类疾病所花费的时间和费用最低,DRGs总权重数和每医师产出最高。不同临床科室主要病种不同。病种顺位前10的各病种时间效率指数最低的科室依次为:D、E、F、C、F、C、E、A、A、E;各病种费用效率指数最低的科室依次为:D、F、F、A、F、C、E、C、A、A。结论DRGs可以对临床专科各临床科室的住院医疗服务能力进行对比分析。
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2006AA020804)Fundamental Research Funds for the Central Universities(No.NJ20120007)+2 种基金Jiangsu Province Science and Technology Support Plan(No.BE2010652)Program Sponsored for Scientific Innovation Research of College Graduate in Jangsu Province(No.CXLX11_0218)Shanghai University Scientific Selection and Cultivation for Outstanding Young Teachers in Special Fund(No.ZZGCD15081)
文摘Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM(IFCM) as well as support vector machines(SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65% and a mean positive predictive value of 97.25%. With an image-based criterion, our approach reached a 100% mean sensitivity, 96.43% mean specificity and 98.21% mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.
