Case-based retrieval to support the treatment of end stage renal failure patients

Montani, Stefania; Portinale, Luigi; Leonardi, Giorgio; Bellazzi, Riccardo; Bellazzi, R.

doi:10.1016/j.artmed.2005.06.003

Cited by 53 publications

(38 citation statements)

References 9 publications

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“…For instance, dimensionality reduction in medical CBR by resorting to DFT has been proposed in [9], where the application domain is the one of haemodialysis. In [9] only the first DFT coefficients are kept for similarity calculation, and Retrieval is implemented as a multi-step procedure. In particular, the most similar cases with respect to each one of the time series features, considered individually, are first extracted.…”

Section: Methods For Dimensionality Reductionmentioning

confidence: 99%

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Case-Based Decision Support in Time Dependent Medical Domains

Montani¹

2010

Artificial Intelligence in Theory and Practice III

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Abstract. Medical applications often require to capture the observed phenomenon dynamics, in order to properly support physicians' decision making. Case-based Reasoning (CBR), and more specifically casebased retrieval, is recently being recognized as a valuable decision support methodology in these domain. However, adopting CBR in this field is non trivial, since the need for describing the process dynamics impacts both on case representation and on the retrieval activity itself. In this work, we survey different methodologies introduced in the literature in order to implement medical CBR applications in time dependent domains, with a particular emphasis on time series representation and retrieval. Among the others, a novel approach, which relies on Temporal Abstractions, is analysed in depth.

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Section: Methods For Dimensionality Reductionmentioning

confidence: 99%

“…In this domain, we compared our TA-based approach with the DFT-based one adopted in [9]. The experiments proved that, when focusing on the shape of time series (rather than on values), the use of TA provides more reliable results.…”

Section: Methods For Dimensionality Reductionmentioning

confidence: 99%

Case-Based Decision Support in Time Dependent Medical Domains

Montani¹

2010

Artificial Intelligence in Theory and Practice III

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“…Montani et al 2 propose a case-base retrieval to support the treatment of ESRD patients. Kusiak et al 3 predict survival time for ESRD patients using data mining.…”

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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“…Other complex representations are possible, such as workflows (Gil (2012)), signals (Montani et al (2006); Olsson et al (2004)), time sequences (Juarez et al (2009)) or graphs (Bunke et al (2005)) among others.…”

Section: Case-base Reasoningmentioning

confidence: 99%

Case-base maintenance with multi-objective evolutionary algorithms

et al. 2015

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CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated.The final publication is available at Springer via http://dx.doi.org/10.1007/s10844-015-0378-z Case-Base Maintenance with Multi-Objective Evolutionary AlgorithmsAbstract Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels.In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a Multi-Objective Evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the CaseBased Reasoning system. Finally, from a set of the experiments, our proposed Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.Eduardo Lupiani · Jose M. Juarez · Jose Palma University of Murcia, Spain E-mail: {elupiani,jmjuarez,jtpalma}@um.es Susan Craw · Stewart Massie Robert Gordon University, Scotland, UK E-mail: {s.craw,s.massie}@rgu.ac.uk 2 E. Lupiani et al.

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Case-based retrieval to support the treatment of end stage renal failure patients

Cited by 53 publications

References 9 publications

Case-Based Decision Support in Time Dependent Medical Domains

Case-Based Decision Support in Time Dependent Medical Domains

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

Case-base maintenance with multi-objective evolutionary algorithms

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