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خانه مقالات-Article مقالات کامپیوتر-Computer Articles داده کاوی-Data Mining A data mining approach for grouping and analyzing trajectories of care using claim data
A data mining approach for grouping and_[taliem.ir]

A data mining approach for grouping and analyzing trajectories of care using claim data

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Background: With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources. We propose an approach based on mining claim data.to support the exploration of trajectories of car

Methods: A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. The main diagnoses of hospitalizations were used to produce morbidity profiles. Cumulative hospital costs were computed for each profile

توضیحات محصول

ABSTRACT

Background: With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources. We propose an approach based on mining claim data.to support the exploration of trajectories of car

Methods: A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. The main diagnoses of hospitalizations were used to produce morbidity profiles. Cumulative hospital costs were computed for each profile
Results: 57,552 patients were automatically grouped into 19 classes. The resulting profiles were clinically meaningful and economically relevant. The mean cost per trajectory was 9,600e. Severe conditions were generally associated with higher costs. The lowest costs (6,957e) were  observed for patients with in situ carcinoma of the breast, the highest for patients hospitalized for palliative care (26,139e)

Conclusions: Formal Concept Analysis can be applied on claim data to produce an automatic classification of care trajectories. This flexible  approach takes advantages of routinely collected data and can be used to setup cost-of-illness studies.

INTRODUCTION

Background
Health-care systems face a crisis of an increasing burden of chronic diseases aggravated by aging populations [1]. It
is of much importance that policy makers and healthcare managers can make decisions based on sufficient knowledge and understanding of chronic care activities. This is especially true in the field of cancer where incidence,therapeutics, practices and costs can vary quickly [2,3]. On the one hand, policy-makers need cost-effectiveness and cost-of-illness analyzes for planning and fair allocation of funding. On the other hand, care providers should be able to adapt their resources and costs while they share patients in multidisciplinary and coordinated Health-care systems face a crisis of an increasing burden of chronic diseases aggravated by aging populations [1]. It
is of much importance that policy makers and healthcare managers can make decisions based on sufficient knowledge and understanding of chronic care activities. This is especially true in the field of cancer where incidence,therapeutics, practices and costs can vary quickly [2,3].On the  one hand, policy-makers need cost-effectiveness and cost-of-illness analyzes for planning and fair allocation of funding. On the other hand, care providers should be able to adapt their resources and costs while
they share patients in multidisciplinary and coordinated

Methods
Formal concept analysis Introduced by Wille [16], Formal Concept Analysis (FCA) is a theory of data analysis identifying conceptual structures  within data sets [17]. FCA is closely related to the well-known Association Rule Mining (ARM) and frequent
itemsets discovery methods [18]. Indeed, many of the most efficient ARM algorithms are FCA-based [19-21].A key advantage of the FCA-like mining lays in the fact that due to closure properties, only patterns of maximal size are extracted. This ability to produce condensed representation of patterns or rules reduces the exploration/interpretation burden for the analyst [22]

Year:2013

Publisher:BMC

By: Nicolas Jay, Gilles Nuemi, Maryse Gadreau and Catherine Quantin

File Information:English Language/ 9 Page / size:344KB

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سال : 2013

ناشر : BMC

کاری از : Nicolas Jay, Gilles Nuemi, Maryse Gadreau and Catherine Quantin

اطلاعات فایل : زبان انگلیسی / 9 صفحه / حجم : 344KB

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