A Systematic Review of Security Mechanisms for Big Data in Health and New Alternatives for Hospitals

Hamrioui, Sofiane; Díez, Isabel de la Torre; Garcia‐Zapirain, Begonya; Saleem, Kashif; Rodrigues, Joel J. P. C.

doi:10.1155/2017/2306458

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…As it has been previously introduced, a good number of systematic reviews can be found in the field of Big Data [18,27] and specific domains such as the health sector [17,18,31] the same manner, scientific programming techniques have been widely studied and reviewed [7,8]. However, the application of Big Data technology and scientific programming techniques to the engineering domain has not been fully reviewed, and it is not easy to draw a comprehensive picture of the current state of the art apart from works in specific domains such as climate [32] or astronomy [20].…”

Section: Review Protocolmentioning

confidence: 99%

“…Last years have also seen a large body of work in the field of Big Data covering from the creation of tools and architectural frameworks to its application to different domains [10][11][12] such as social network analysis [13], bioinformatics [14], earth science [15], e-government [16], e-health [17,18], or e-tourism [19]. More specifically, in the context of engineering and industry, some works have been reported [20] by large companies such as Teradata in particular domains such as maintenance of aircraft engines.…”

Section: Introductionmentioning

confidence: 99%

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Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.

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Section: Review Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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“…As Machine Learning (ML) is a technique consisting of a dataset that identifies relationships between features and algorithm outputs, it was applied in this study. By using algorithms, it is possible to develop techniques that allow the computer to "learn" to classify features, creating algorithms capable of generalizing data from unstructured information as samples [4,5]. Machine learning is very useful in the early diagnosis of affliction and diseases.…”

Section: Introductionmentioning

confidence: 99%

“…A supervised learning algorithm analyzes the input data (characteristics analyzed) and produces an output which is the variable being predicted [6,7]. By selecting the features, relationships and patterns can be established between the data and features about which we wish to make a prediction [5,8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Cristian

Zúñiga

2020

IJERPH

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The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe.

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“…A set of data can be used to identify links between algorithm attributes and outputs. Using feature selection, it is possible to establish links and patterns between data and the attribute about which one wishes to make the prediction [17,18]. There is a great variety of algorithms used in machine learning, some of which enjoy major popularity, namely, Nearest Neighbors, Linear SVM (Support Vector Machines), RBF (Radial Basis Function) SVM, Gaussian Process RBF, Decision Tree, Random Forest, AdaBoost, and Gaussian Naive Bayes.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital

Cristian

Soto

Lozano

et al. 2019

IJERPH

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This paper presents a study based on data analysis of the sarcopenia level in older adults. Sarcopenia is a prevalent pathology in adults of around 50 years of age, whereby the muscle mass decreases by 1 to 2% a year, and muscle strength experiences an annual decrease of 1.5% between 50 and 60 years of age, subsequently increasing by 3% each year. The World Health Organisation estimates that 5–13% of individuals of between 60 and 70 years of age and 11–50% of persons of 80 years of age or over have sarcopenia. This study was conducted with 166 patients and 99 variables. Demographic data was compiled including age, gender, place of residence, schooling, marital status, level of education, income, profession, and financial support from the State of Baja California, and biochemical parameters such as glycemia, cholesterolemia, and triglyceridemia were determined. A total of 166 patients took part in the study, with an average age of 77.24 years. The purpose of the study was to provide an automatic classifier of sarcopenia level in older adults using artificial intelligence in addition to identifying the weight of each variable used in the study. We used machine learning techniques in this work, in which 10 classifiers were employed to assess the variables and determine which would provide the best results, namely, Nearest Neighbors (3), Linear SVM (Support Vector Machines) (C = 0.025), RBF (Radial Basis Function) SVM (gamma = 2, C = 1), Gaussian Process (RBF (1.0)), Decision Tree (max_depth = 3), Random Forest (max_depth=3, n_estimators = 10), MPL (Multilayer Perceptron) (alpha = 1), AdaBoost, Gaussian Naive Bayes, and QDA (Quadratic Discriminant Analysis). Feature selection determined by the mean for the variable ranking suggests that Age, Systolic Arterial Hypertension (HAS), Mini Nutritional Assessment (MNA), Number of chronic diseases (ECNumber), and Sodium are the five most important variables in determining the sarcopenia level, and are thus of great importance prior to establishing any treatment or preventive measure. Analysis of the relationships existing between the presence of the variables and classifiers used in moderate and severe sarcopenia revealed that the sarcopenia level using the RBF SVM classifier with Age, HAS, MNA, ECNumber, and Sodium variables has 82′5 accuracy, a 90′2 F1, and 82′8 precision.

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A Systematic Review of Security Mechanisms for Big Data in Health and New Alternatives for Hospitals

Cited by 11 publications

References 33 publications

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital

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