DataCare: Big Data Analytics Solution for Intelligent Healthcare Management

Gómez, Alejandro Baldominos; Rada, Fernando de; Sáez, Yago

doi:10.9781/ijimai.2017.03.002

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…Three techniques measure the efficiency in the interaction of the user with the computer, the memory capacity that a user presents after browsing a website that he had not visited for a while, and the efficiency performing tasks on a website. These assess satisfaction by analyzing the facial expressions of users [19].…”

Section: Usability Evaluationmentioning

confidence: 99%

“…After publishing this orderly grouping of recommendations [8] and evaluating the recommendations of the group (1) recommendations to reduce "noise" in "Compliance analysis and usability application and recommendations of web developers" [8], the recommendations of the group (4) efficient and understandable controls for users to enter information in "Analysis of compliance and application of usability guidelines on efficient and understandable controls" [9], group recommendations (2) follow the conventions, this document has the purpose of grouping, and analyzing group recommendations (3) give information quickly and comprehensively. Of those 103 recommendations, there are 24 useful recommendations on (3) give information quickly and comprehensively, all of which have been extracted from [10][11][12][13][14][15][16][17][18][19][20]. User tests and interviews with web developers are conducted without training in web usability to evaluate each of the recommendations.…”

Section: Introductionmentioning

confidence: 99%

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Compliance and application tests of usability guidelines about giving information quickly and comprehensibly

Alonso-Virgós

Espada

Martínez

et al. 2020

Complex Intell. Syst.

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The objective of this publication is to analyze compliance with the web usability guidelines on Giving information quickly and comprehensibly. The behavior of 20 IT engineers without experience in web usability is analyzed to collect data on the application and compliance of each of the studied guidelines. The objectives are: (1) make a list of recommendations on the presentation of information and possible actions in a quickly understandable way. And highlight the most forgotten guidelines or the worst followed by web developers to think about the importance of offering specific training in this field. (2) Know the most important guidelines according to the participants themselves. To obtain the results, user tests are performed that evaluate the most ignored and applied guidelines. And its correct compliance is studied, since the participants do not have experience in web usability. Besides, interviews are conducted to find out which are the guidelines that they consider most important. It is expected to know if there are guidelines that apply intuitively and why. It is also intended to know if this innate application is helpful or compliance is wrong and needs specific training.

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Section: Usability Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compliance and application tests of usability guidelines about giving information quickly and comprehensibly

Alonso-Virgós

Espada

Martínez

et al. 2020

Complex Intell. Syst.

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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 authors in [9] proposed a smart Healthcare Management System named ''DataCare'', to be used at hospitals or healthcare centers, with the aim to increase patient satisfaction by monitoring clinical Key Performance Indicators (KPIs) and identifying unexpected situations. The architecture of the system has three main modules: Data Retrieving and Aggregation, Data Processing and Analysis, and Data Visualization.…”

Section: F Healthcare Management Systemmentioning

confidence: 99%

BIG DATA for Healthcare: A Survey

et al. 2019

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Recently, the massification of new technologies, which has been adopted by a large majority of the world population, has accumulated a tremendous amount of data, including clinical data. This clinical data have been gathered up and interpreted by medical organizations in order to gain insights and knowledge useful for clinical decisions, drug recommendations, and better diagnoses, among many other uses. This paper highlights the enormous impacts of big data on medical stakeholders, patients, physicians, pharmaceutical and medical operators, and healthcare insurers, and also reviews the different challenges that must be taken into account to get the best benefits from all this big data and the available applications. INDEX TERMS Big data, big data analytics, healthcare.

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DataCare: Big Data Analytics Solution for Intelligent Healthcare Management

Cited by 12 publications

References 12 publications

Compliance and application tests of usability guidelines about giving information quickly and comprehensibly

Compliance and application tests of usability guidelines about giving information quickly and comprehensibly

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

BIG DATA for Healthcare: A Survey

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