A New Clustering Approach for Symbolic Data and Its Validation: Application to the Healthcare Data

Elghazel, Haytham; Deslandres, Véronique; Hacid, Mohand-Saïd; Dussauchoy, A.; Kheddouci, Hamamache

doi:10.1007/11875604_54

Cited by 25 publications

(18 citation statements)

References 7 publications

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“…This paper reports the work in adopting the Markov Models and a recently proposed b-coloring based clustering approach [1] for discovering a typology of clinical pathways in the French medical information system.…”

Section: Context and Motivationsmentioning

confidence: 99%

“…In order to divide the vertex set V into a partition , , … , where for ∀ C i ,C j ∈ P, C i ∩ C j =∅ for i≠j (when the number of clusters k is not pre-defined), our new clustering approach [1], based on graph b-coloring is applied to the graph G. A graph b-coloring is the assignment of colors (clusters) to the vertices of the graph such that: (i) no two adjacent vertices have the same color (proper coloring), (ii) for each color there exists at least one dominating vertex which is adjacent to all the other colors.…”

Section: Clinical Pathways Clusteringmentioning

confidence: 99%

“…For our concern, each hospital stay is characterized by two qualitative items: the Diagnosis Related Group and the Principal Medical Diagnosis. The population of clinical pathway will be divided into k clusters using the b-coloring based clustering method in [1]. The later enables to build a fine partition of the data set even if the number of clusters k is not specified in advance.…”

Section: Context and Motivationsmentioning

confidence: 99%

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Clinical pathway analysis using graph-based approach and Markov models

Elghazel

Deslandres

Kallel

et al. 2007

2007 2nd International Conference on Digital Information Management

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Cluster analysis is one of the most important aspects in the data mining process for discovering groups and identifying interesting distributions or patterns over the considered data sets. A new method for sequences clustering and prediction is presented in this paper, which is based on a hybrid model that uses our b-coloring based clustering approach as well as Markov chain models. The paper focuses on clinical pathway analysis but the method applies to every kind of sequences, and a generic decision support framework has been developed for managers and experts. The interesting result is that the clusters obtained have a twofold representation. Firstly, there is a set of dominant sequences which reflects the properties of the cluster and also guarantees that clusters are well separated within the partition. On the other hand, the behavior of each cluster is governed by a finite-state Markov chain model which allows probabilistic prediction. These models can be used for predicting possible paths for a new patient, and for helping medical professionals to eventually react to exceptions during the clinical process. Context and MotivationsIn French hospitals, the Diagnosis Related Groups (DRG) system was introduced in the eighties with the new medical information system provision called PMSI. DRG classification is a national decision and provides a certain unity in economic (cost similarity) and medical terms. Hence PMSI data can also be used for the assessment of both public and private hospital performances. Each hospital stay leads to one standard uniform discharge summary (SDS). The SDS contains data related to the nature of treatments, medical exams and diagnosis as well as patient's personal information. The SDS is then identified to correspond to one patient's group called the Diagnosis Related Group (DRG). This operation is performed using a supervised approach according to a decision tree.The PMSI system, which involves both economic and medical data, offers a good starting point for designing a suitable strategic information system according to healthcare quality and cost control. Due to the prevalence of information technology in medical care, data collected from PMSI can be used for discovering useful patterns and especially for clinical pathway. These patterns can be analyzed automatically or by medical professionals in order to develop better strategies to improve the quality of medical treatments.Indeed, the numerous medical activities, and therapeutic interventions performed by medical professionals and collected from PMSI may be considered as a medical histories. These records are valuable sources for anticipating future trends of clinical pathways, in order to facilitate resources and activities planning and management within French health centers.This paper reports the work in adopting the Markov Models and a recently proposed b-coloring based clustering approach [1] for discovering a typology of clinical pathways in the French medical information system.The key idea of this paper is to formulate ...

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Section: Context and Motivationsmentioning

confidence: 99%

Section: Clinical Pathways Clusteringmentioning

confidence: 99%

Section: Context and Motivationsmentioning

confidence: 99%

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Clinical pathway analysis using graph-based approach and Markov models

Elghazel

Deslandres

Kallel

et al. 2007

2007 2nd International Conference on Digital Information Management

Self Cite

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“…The b-coloring based clustering method enables to build a fine partition of the dataset into clusters even when the number of clusters is not specified in advance. The previous clustering algorithm in (Elghazel et al, 2006) conducts the following two steps in greedy fashion: 1. initalizes the colors of vertices so that the colors satisfy proper coloring, and 2. removes, by a greedy procedure, the colors that have no dominating vertices, until each color has at least one dominating vertex. These steps correspond to the above two constraints in b-coloring.…”

Section: Introductionmentioning

confidence: 99%

“…In order to alleviate this weakness, we have proposed a greedy algorithm which realizes the re-coloring of data items (vertices) to improve the quality of the constructed partition (Elghazel et al, 2007). Informally, our algorithm selects at each stage the vertex with the maximum degree of "outlier" and which do not affects the dominant vertices in the bcoloring.…”

Section: Introductionmentioning

confidence: 99%

Toward Improving b-Coloring Based Clustering Using a Greedy re-Coloring Algorithm

Yoshida¹,

Elghazel²,

Deslandres³

et al. 2008

Greedy Algorithms

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Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

Pérez¹,

Corchado²,

Mathieu³

et al. 2014

Advances in Intelligent Systems and Computing

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The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing.The publications within "Advances in Intelligent Systems and Computing" are primarily textbooks and proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. Advisory BoardChairman Nikhil R. Pal,

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A New Clustering Approach for Symbolic Data and Its Validation: Application to the Healthcare Data

Cited by 25 publications

References 7 publications

Clinical pathway analysis using graph-based approach and Markov models

Clinical pathway analysis using graph-based approach and Markov models

Toward Improving b-Coloring Based Clustering Using a Greedy re-Coloring Algorithm

Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

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