CADRE: Cloud-Assisted Drug REcommendation Service for Online Pharmacies

Zhang, Yin; Zhang, Daqiang; Hassan, Mohammad Mehedi; Alamri, Atif; Peng, Limei

doi:10.1007/s11036-014-0537-4

Cited by 78 publications

(30 citation statements)

References 11 publications

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“…With regard to healthcare, there are three different domains of exploiting cognitive computing techniques, i.e., physiological healthcare [20,24,25], psychological healthcare [7,19,23,26,28] as well as medicine analysis [21,22]. In order to provide an advanced healthcare service.…”

Section: Related Workmentioning

confidence: 99%

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A 5G Cognitive System for Healthcare

Chen

Yang

Hao

et al. 2017

BDCC

125

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Developments and new advances in medical technology and the improvement of people's living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this issue, a video conference-based telemedicine system is deployed to break the limitations of medical resources in terms of time and space. By outsourcing medical resources from big hospitals to rural and remote ones, centralized and high quality medical resources can be shared to achieve a higher salvage rate while improving the utilization of medical resources. Though effective, existing telemedicine systems only treat patients' physiological diseases, leaving another challenging problem unsolved: How to remotely detect patients' emotional state to diagnose psychological diseases. In this paper, we propose a novel healthcare system based on a 5G Cognitive System (5G-Csys). The 5G-Csys consists of a resource cognitive engine and a data cognitive engine. Resource cognitive intelligence, based on the learning of network contexts, aims at ultra-low latency and ultra-high reliability for cognitive applications. Data cognitive intelligence, based on the analysis of healthcare big data, is used to handle a patient's health status physiologically and psychologically. In this paper, the architecture of 5G-Csys is first presented, and then the key technologies and application scenarios are discussed. To verify our proposal, we develop a prototype platform of 5G-Csys, incorporating speech emotion recognition. We present our experimental results to demonstrate the effectiveness of the proposed system. We hope this paper will attract further research in the field of healthcare based on 5G cognitive systems.

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Section: Related Workmentioning

confidence: 99%

“…Chen et al [23] utilized a pillow robot, a 5G network as well as a cloud platform to achieve real-time emotional interaction between remote users. The cognitive computing technique is also applied to drug recommendation in [21,22], in order to provide an adequate medical service to both doctors and patients.…”

Section: Related Workmentioning

confidence: 99%

A 5G Cognitive System for Healthcare

Chen

Yang

Hao

et al. 2017

BDCC

125

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“…We have chosen this algorithm because it is widely used to recommend items when the modeling of user preferences is not a valid option (as in most of real scenarios [17][18][19] and others [20][21][22][23]). This algorithm requires users to rate an initial set of items.…”

Section: Group Recommendation Systems Grsmentioning

confidence: 99%

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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With the development of social networks and online mobile communities, group recommendation systems support users' interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users' personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external socialtrust networks is proposed, which builds a group profile by analyzing not only users' preferences, but also the social relationships between members inside and outside of the group. We employ the users' degree of disagreement to adjust group preference rating by external information of social-trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.

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“…-The samples are unknown label as negative samples, but set a lower weight, the weight of the sample is negative reflecting the confidence level of the sample [7]. But the disadvantage of this approach is that it can not guarantee "true".…”

Section: Negative Feedbackmentioning

confidence: 99%

Implicit Feedback Recommender System Based on Matrix Factorization

Jiang

2018

Proceedings of the 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks &

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With the development of the internet age, information overload problem is imminent. At now, almost of recommended models use the explicit feedback. But lots of implicit feedback data are missing. The paper explores the area of recommendation based on large-scale implicit feedback, Where only positive feedback is available. Further, the paper carried on the empirical research on the Implicit Feedback Recommendation Model. By maximized the probability of the user's choices, IFR mean the progress task into optimization problems In the way, the experiment results confirm the superiority of the model. However, the model is insufficient about online research and a lack of details.

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CADRE: Cloud-Assisted Drug REcommendation Service for Online Pharmacies

Cited by 78 publications

References 11 publications

A 5G Cognitive System for Healthcare

A 5G Cognitive System for Healthcare

Group Recommendation Systems Based on External Social‐Trust Networks

Implicit Feedback Recommender System Based on Matrix Factorization

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