Dynamic Fuzzy-Neural based tool formonitoring and predicting patients conditions using selected vital signs

Schatz, Cecilia H. Vallejos de; Schneider, Fábio Kurt; Abatti, Paulo José; Nievola, Júlio César

doi:10.3233/ifs-151537

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Starting from the Human-Robot Interaction research, it is possible to develop a system that employs machine learning algorithms in order to feature a humanoid robot Health Coach that helps to reduce childhood obesity [1]. Other example of this kind of application is chronic disease with elderly patients [18]. As in the case of locomotion problems, the medical community does not have enough resources to decide the best treatment for chronic diseases.…”

Section: Use and Application Of Collective Intelligence In Ehealthmentioning

confidence: 99%

Collective intelligence and databases in eHealth: A survey1

Camacho

Merayo

Núñez

2017

IFS

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Healthcare is one of the most important concerns of society, being extremely relevant the accuracy and quality of their services. Basically, eHealth can be considered as the area where electronic processes and communications are used to improve the quality of medical assistance. Despite the relative maturity of the field, recalls and problems related to medical devices, applications and services are still very frequent. Therefore, it is necessary to improve and expand current research to provide advances that can be transmitted to the society. As an initial step, it is essential to have a good understanding of the current state-of-the-art of eHealth. The main goal of this paper is to identify the most relevant and recent work on eHealth but, due to the immensity of the field and the scope of the journal, we will concentrate on the use of two specific technologies: databases and collective intelligence. In addition to review the main concepts related to eHealth and the most influent academic papers, we will describe projects which are essential for the correct performance of many healthcare services. As a result of our study, we reached some interesting conclusions that might be useful for future projects and we encourage to apply them in order to avoid some of the problems that we have found in existing projects.

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Section: Use and Application Of Collective Intelligence In Ehealthmentioning

confidence: 99%

Collective intelligence and databases in eHealth: A survey1

Camacho

Merayo

Núñez

2017

IFS

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“…These systems are gaining popularity in Medicine, in particular when used to evaluate the quality of health care systems [1] and monitor the progress of patients [29]. In the latter case, it is necessary to take into account the special nature of vital signs so that the best performance to predict patient conditions can be obtained when using a fuzzy model.…”

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confidence: 99%

Collective intelligent information and database systems

Nguyen

Núñez

Trawiński

2017

IFS

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Abstract. Collective intelligence is most often understood as group intelligence which arises on the basis of intelligences of the group members. This paper presents an overview of application of collective intelligence methods in knowledge engineering and in processing collective data. It also introduces papers included in this issue. Keywords: Collective intelligence, intelligent systems, knowledge engineeringCollective intelligence is most often understood as group intelligence which arises on the basis of intelligences of the group members. On the one hand, Newell [19] defined an intelligent collective as a social system, which is capable to act, even approximately, as a single rational agent. On the other hand, Lévy [11] understood collective intelligence as an intelligence that emerges from the collaboration and competition of many individuals; an intelligence that seemingly has a mind of its own. These definitions refer to cognitive systems. From the computational and artificial intelligence point of view, we can think about a collective as a set of autonomous units working on some common task, for example, a multi-agent system. We can say that the collective is intelligent if it can make a use of the intelligences of its members for solving some problem, for example, a decision making problem. are based on the collective intelligence aspects and which use the computational techniques for solving these problems. Regarding knowledge engineering, computational collective intelligence provides methods and techniques for determining the knowledge of a collective as a whole on the basis of collective members' knowledge. The need for processing collective knowledge is quickly increasing because of the very fast development of Internet, social networks and distributed databases. It is obvious that knowledge originating from autonomous sources for the same subject is very often inconsistent. Therefore, the aspects of inconsistency processing and integration computing are very important.Knowledge engineering [9] plays a relevant role in CCI since it is necessary to use its techniques in order to represent individual information. In fact, there are studies advocating that scientific knowledge is essentially collective knowledge [23]. In this line, recent studies state that it is very important that different individuals provide orthogonal, highly unrelated, and possible contradictory knowledge to the collectivity. In other words, "the higher the inconsistency, the better the quality of collective knowledge" [20]. Another relevant interaction

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Dynamic Fuzzy-Neural based tool formonitoring and predicting patients conditions using selected vital signs

Cited by 2 publications

References 28 publications

Collective intelligence and databases in eHealth: A survey1

Collective intelligence and databases in eHealth: A survey1

Collective intelligent information and database systems

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