The Potential Use of Multi-agent and Hybrid Data Mining Approaches in Social Informatics for Improving e-Health Services

Sharma, Dharmendra; Shadabi, Fariba

doi:10.1109/bdcloud.2014.25

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…The authors of [3] presented a multi-agent based intelligent method for controlling the health status of older peoples. In recent decades, multi-agent networks have been a prominent issue [2,10]. It is being designed and analysed since it has a significant impact on society.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It's also likely that numerous forms of dangers, like malware, Trojan, and adware, can readily get access to public health system. The researchers of [2] presented a better access control mechanism and its application. The primary objective of this strategy is to make accessing private data relatively protected.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, consumers such as medical personnel can view such stored medical information, which contain the majority of private and sensitive health information. Various methodologies for protecting the privacy and protection of the EHR [1,2] have been suggested previously. However, in order to transmit health data, these approaches need more protection.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Methodology for Providing Security in Electronic Health Record Using Fuzzy Based Multi Agent System

Alanezi¹

2021

Int. J. Onl. Eng.

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<p class="0abstract">Privacy and security are crucial when it comes to the implementation of Electronic Healthcare Records (HER). Whenever a patient visits a doctor, their health records are updated since it consists of vital information on the patient's health and well-being. It also comprises of description about the previous and current treatment quality.Multi-Agent Systems can support E-Healthcare applications for improving quality of life. The availability and use of extensive systems for EHR has increased dramatically. The EHR technology currently available consists of health information of various patients which helps those people to monitor their health. These information should be protected from being altered or mishandled. Various studies have been conducted in the past literature to improve the protection and privacy requirements in E-health services. Despite this, it hasn't been entirely improved. This article proposes a Fuzzy based multi-agent system architecture that uses agents and fuzzy logic to monitor the flow and access of the EHR record using fuzzy logic and multi agents. Architecture comprises of multi agent and fuzzy systems are proposed. In this system, we have employed Action/ Computation (Interface) agent, communication interface agent and verification agent and Communication Module Agent. These agents collaboratively worked one another in order to obtain the desired output. These agent based e-health system can increase the accuracy of data transfer by providing additional security in selecting the user who handles the data. It also monitors the communication between different medical units in the health system.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Methodology for Providing Security in Electronic Health Record Using Fuzzy Based Multi Agent System

Alanezi¹

2021

Int. J. Onl. Eng.

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“…Here, the team focuses on details of the projects. Because of the rise in project's cost in comparison to traditional ones, the team must do an economic feasibility study and explain why the project is cost-effective [26][27][28]. The project team should also provide background information on the problem domain as well as prior projects and research performed in this domain.…”

Section: Designing Health Recommender Systemmentioning

confidence: 99%

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

et al. 2019

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In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

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“…The only field of study currently taught that bears the original name is Health and Social Informatics ( helse- og sosialinformatikk ) at the University of Agder. Recent papers [52, 53] draw attention to the importance and benefits of cooperation between health and social informatics. In Denmark in 1985, the University of Aalborg created the study programme humanistic informatics ( humanistisk informatik ) under the influence of the Norwegian School.…”

Section: Development Of Individual Concepts Of Social Informaticsmentioning

confidence: 99%

Social informatics as a concept: Widening the discourse

Smutný

2016

Journal of Information Science

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This contribution examines the different concepts known as social informatics that have historically been separate. The paradigm that is preferred worldwide (based on Kling) is well described and often promoted, with a strong base both in the USA and Europe. This article, however, introduces lesser-known paradigms (based on Sokolov and later Ursul) that originated in the era of the USSR and have so far been employed chiefly in post-Soviet countries, including Russia. These paradigms have been neglected in English-written scientific literature, mainly because of the limited number of articles available in English. Other approaches are also introduced and related, which were historically named or classified as social informatics (American, British, Norwegian, Slovenian, German and Japanese). The present article introduces and further discusses the origin, historical development and basic methodological grounding of these approaches. All the approaches are then discussed and their differences as well as their similarities are pointed out. The aim is to create connections across the current generation of researchers, which includes the formation and conceptualization of different approaches and an exploration of possible areas for future cooperation.

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The Potential Use of Multi-agent and Hybrid Data Mining Approaches in Social Informatics for Improving e-Health Services

Cited by 4 publications

References 20 publications

A Novel Methodology for Providing Security in Electronic Health Record Using Fuzzy Based Multi Agent System

A Novel Methodology for Providing Security in Electronic Health Record Using Fuzzy Based Multi Agent System

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Social informatics as a concept: Widening the discourse

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