Objective： To evaluate the effect of Internet technology on continuing nursing in elderly patients with diabetic feet, Method： From January 2015 to July 2016,12 elderly patients with diabetic foot ulcers were enroll...Objective： To evaluate the effect of Internet technology on continuing nursing in elderly patients with diabetic feet, Method： From January 2015 to July 2016,12 elderly patients with diabetic foot ulcers were enrolled from the Endocrinology Department in our hospital. We used ＂WeChat＂, ＂E nursing＂ and other Internet technologies to perform remote extended care and to observe the foot ulcer outcomes. Results： All foot ulcers healed with a wound healing time between 38 and 73 days （average 57.08 ~ 12.69 days）. Patients did not need to travel long distances to seek medical treatment for foot ulcers, improving their satisfaction. Conclusions： The implementation of extended care for elderly patients with diabetic foot ulcers was based on the application of Internet technology. It is helpful to facilitate medical treatment, share high quality health resources and promote disease rehabilitation.展开更多
We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution...We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F(KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means(FCM): sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.展开更多
基金supported by Science and Technology Commission of Shanghai Municipality(No.16411971300)Key Laboratory of Geriatrics of Shanghai Municipality(No.13dz2260700)
文摘Objective： To evaluate the effect of Internet technology on continuing nursing in elderly patients with diabetic feet, Method： From January 2015 to July 2016,12 elderly patients with diabetic foot ulcers were enrolled from the Endocrinology Department in our hospital. We used ＂WeChat＂, ＂E nursing＂ and other Internet technologies to perform remote extended care and to observe the foot ulcer outcomes. Results： All foot ulcers healed with a wound healing time between 38 and 73 days （average 57.08 ~ 12.69 days）. Patients did not need to travel long distances to seek medical treatment for foot ulcers, improving their satisfaction. Conclusions： The implementation of extended care for elderly patients with diabetic foot ulcers was based on the application of Internet technology. It is helpful to facilitate medical treatment, share high quality health resources and promote disease rehabilitation.
基金Project supported by the National Research Foundation(NRF) of Korea(Nos.2013009458 and 2013068127)
文摘We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F(KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means(FCM): sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.
