Temporal data mining for the quality assessment of hemodialysis services

Bellazzi, Riccardo; Larizza, Cristiana; Magni, Paolo; Bellazzi, R.

doi:10.1016/j.artmed.2004.07.010

Cited by 92 publications

(67 citation statements)

References 28 publications

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“…Bellazzi et al 12 do a temporal data mining for the quality assessment of hemodialysis services. Bellazzi et al 13 implement an automated system for monitoring adherence to hemodialysis treatment.…”

Section: Social Variablesmentioning

confidence: 99%

Application of K-Means Technique in Data Mining to Cluster Hemodialysis Patients

Ghodsi

Marani

Keramati

2017

IRATJ

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Hemodialysis patients with end stage renal disease are often hospitalized due to infection, cardiovascular syndromes, or cancer. Through interviews with the doctors at a well-known hospital for kidney disease, a list of important variables including the clinical lab test results, the demographic and the socio-economic information was developed. After finalizing the list with the doctors, the needed data of 50 patients was provided anonymously so that real data can be used for this research. The review of literature reveals that no other research considered all of these measures used in this work simultaneously. In the next stage, the data was processed and prepared to be used as the input data for the implementation of data mining techniques. The clustering of patients via the K-Means technique is explained here. The results obtained from the data mining model in this research can be useful for the decision makers and doctors. Implementing the model seems promising and analyzing the results proved this point and delivered interesting grouping of patients. Also, it can be claimed that this research is a good prologue for future researches.

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Section: Social Variablesmentioning

confidence: 99%

Application of K-Means Technique in Data Mining to Cluster Hemodialysis Patients

Ghodsi

Marani

Keramati

2017

IRATJ

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“…This has also been reviewed e.g. [18], [19]; [20]; and [21]. In literature the process of DM in the field of medicine and other domains, such as business, marketing and the economy has also been differentiated and elucidated e.g.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Data Mining Techniques for Predicting Immunize-able Diseases: Nigeria as a Case Study

PeterIdowu¹,

Akhigbe²,

Adeosun³

et al. 2013

IJAIS

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Disease rates vary between different locations particularly in the rural areas. While a database of diseases occurrence could be easily found, studies have been limited to descriptive statistical analysis, and are mostly restricted to diseases affecting adults. This paper therefore presents a Mathematical Model (MM) for predicting immunize-able diseases that affect children between ages 0 -5 years. The model was adapted and deployed for use in six (6) selected localized areas within Osun State in Nigeria. Using the MATLAB's ANN toolbox, the Statistics toolbox for classification and regression, and the Naïve Bayesian classifier the MM was developed. The MM is robust in that it takes advantage of three (3) data mining techniques: ANN, Decision Tree Algorithm and Naïve Bayes Classifier. These data mining techniques provided the means by which hidden information were discovered for detecting trends within databases, and thus facilitate the prediction of future disease occurrence in the tested locations. Results obtained showed that diseases have peak periods depending on their epidemicity, hence the need to adequately administer immunization to the right places at the right time. Therefore, this paper argues that using this model would enhance the effectiveness of routine immunization in Nigeria.

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“…On the other hand, data mining techniques for discovering AR in time series can be found in Bellazzi et al (2005). The authors successfully mined temporal data retrieved from multiple hemodialysis sessions by applying preprocessing, data reduction and filtering as a previous step of the AR extraction process.…”

Section: Related Workmentioning

confidence: 99%

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

Martínez-Ballesteros¹,

Martínez‐Álvarez²,

Troncoso³

et al. 2011

Soft Comput

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An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative association rules. This algorithm, called QARGA (Quantitative Association Rules by Genetic Algorithm), uses a particular codification of the individuals that allows solving two basic problems. First, it does not perform a previous attribute discretization and, second, it is not necessary to set which variables belong to the antecedent or consequent. Therefore, it may discover all underlying dependencies among different variables. To evaluate the proposed algorithm three experiments have been carried out. As initial step, several public datasets have been analyzed with the purpose of comparing with other existing evolutionary approaches. Also, the algorithm has been applied to synthetic time series (where the relationships are known) to analyze its potential for discovering rules in time series. Finally, a real-world multidimensional time series composed by several climatological variables has been considered. All the results show a remarkable performance of QARGA.

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Temporal data mining for the quality assessment of hemodialysis services

Cited by 92 publications

References 28 publications

Application of K-Means Technique in Data Mining to Cluster Hemodialysis Patients

Application of K-Means Technique in Data Mining to Cluster Hemodialysis Patients

Data Mining Techniques for Predicting Immunize-able Diseases: Nigeria as a Case Study

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

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