With popularity of smart devices, people can more easily access to medical data from the underlying medical equipment and sent to the remote monitoring system or professional medical staff in smart health. However, with the continuous expansion of smart health data, the public transport protocol can't meet the requirements of special medical services, such as http protocol have a complex structure. Moreover, traditional server applications face great challenges of high concurrency and high throughput. To address these issues, this paper achieve rapid transmission of medical data services based on the Netty private protocol stack research, which combined with the network protocol architecture of the chain of responsibility. Each layer has a processing interface, different operations and avoid coupling the sender of a request to its receiver can be perform by giving more than one object a chance to handle the request. Tests show that a single server supports 45000 Medical Device Manager, which accesses with a message delivery rate of 100% and a message delay of less than 5 seconds.
The prediction of patient's future health information from the historical electronic health records (EHR) forms the core of the development of personalized healthcare research tasks.Patient EHR data consists of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and patient profile.Using historical data from the EHR, we can predict medical conditions and medication uses.Existing works model EHR data by using recurrent neural networks (RNNs).However, RNN-based approaches have certain limitations: the performance of RNNs drops when the length of sequences is large and they ignore some of the characteristics of the patients themselves.We propose an application of using bidirectional RNNs to remember all the information of both the past and future visits and add some patient's characteristics as side information into this model.Experimental results on real world EHR datasets show that the proposed model can remarkably improve the prediction accuracy when compared with the diagnosis prediction approaches, and it can provide clinically meaningful interpretation.
Collaborative filtering algorithm is one of the most widely used algorithms in recommender systems and has demonstrated promising results. But it relies too much on similarity to find the nearest neighbors. Whatever, the trust between users is also an import factor needed to be considered. This paper proposed a collaborative filtering algorithm that combined the user similarity and trust to obtain a more appropriate nearest neighbors set. Users not only have same interests as their nearest neighbors, but also have higher level of acceptance in the items recom-mended by their nearest neighbors. Extensive experiments based on Film Trust and MovieLens datasets have shown that the approach has major potential in improving the accuracy of recommended item.
Collaborative filtering algorithm is one of the most widely used algorithms in recommender systems and has demonstrated promising results. But it relies too much on similarity to find the nearest neighbors. Whatever, the trust between users is also an import factor needed to be considered. This paper proposed a collaborative filtering algorithm that combined the user similarity and trust to obtain a more appropriate nearest neighbors set. Users not only have same interests as their nearest neighbors, but also have higher level of acceptance in the items recom-mended by their nearest neighbors. Extensive experiments based on Film Trust and MovieLens datasets have shown that the approach has major potential in improving the accuracy of recommended item.
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