Knowledge discovery in medicine: Current issue and future trend

•This paper reviews data mining application in medicine from 1999 to 2013.•Medical data mining definition and its main goals and challenges are clarified.•Comprehensive categorization is presented to complete previous works and integrate medical and data mining issues.•Each paper is studied from six...

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Veröffentlicht in:Expert systems with applications Jg. 41; H. 9; S. 4434 - 4463
Hauptverfasser: Esfandiari, Nura, Babavalian, Mohammad Reza, Moghadam, Amir-Masoud Eftekhari, Tabar, Vahid Kashani
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Ltd 01.07.2014
Elsevier
Schlagworte:
Applied sciences
Biological and medical sciences
Computer science; control theory; systems
Computerized, statistical medical data processing and models in biomedicine
Data mining
Data mining algorithms
Data mining application
Data processing. List processing. Character string processing
Disease
Exact sciences and technology
Extraction
Medical
Medical data mining
Medical management aid. Diagnosis aid
Medical sciences
Medicine
Memory organisation. Data processing
Regression
Software
Tasks
Trends
Medicine
Data mining algorithms
Medical data mining
Disease
Data mining application
Medical data
Knowledge engineering
Data type
Prognosis
Information extraction
Data mining
Relevance
Biomedical data processing
Classification
Knowledge discovery
Diagnosis
Medical application
Monitoring
Multidisciplinary
User need
Data analysis
Abstract
Cluster
Record format
Medical screening
Knowledge extraction
Standards
Surveillance
User requirement
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Abstract •This paper reviews data mining application in medicine from 1999 to 2013.•Medical data mining definition and its main goals and challenges are clarified.•Comprehensive categorization is presented to complete previous works and integrate medical and data mining issues.•Each paper is studied from six medical tasks and in each tasks, five data mining approaches are considered.•A discussion deals with previous research outline and future trend. Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction. Data mining is supposed to be able to handle various data types in all formats. Relevance of this paper is emphasized by the fact that data mining is an object of research in different areas. In this paper, we review previous works in the context of knowledge extraction from medical data. The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners. Medical data mining is a multidisciplinary field with contribution of medicine and data mining. Due to this fact, previous works should be classified to cover all users’ requirements from various fields. Because of this, we have studied papers with the aim of extracting knowledge from structural medical data published between 1999 and 2013. We clarify medical data mining and its main goals. Therefore, each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management. In each task, five data mining approaches are considered: classification, regression, clustering, association and hybrid. At the end of each task, a brief summarization and discussion are stated. A standard framework according to CRISP-DM is additionally adapted to manage all activities. As a discussion, current issue and future trend are mentioned. The amount of the works published in this scope is substantial and it is impossible to discuss all of them on a single work. We hope this paper will make it possible to explore previous works and identify interesting areas for future research.
AbstractList Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction. Data mining is supposed to be able to handle various data types in all formats. Relevance of this paper is emphasized by the fact that data mining is an object of research in different areas. In this paper, we review previous works in the context of knowledge extraction from medical data. The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners. Medical data mining is a multidisciplinary field with contribution of medicine and data mining. Due to this fact, previous works should be classified to cover all users' requirements from various fields. Because of this, we have studied papers with the aim of extracting knowledge from structural medical data published between 1999 and 2013. We clarify medical data mining and its main goals. Therefore, each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management. In each task, five data mining approaches are considered: classification, regression, clustering, association and hybrid. At the end of each task, a brief summarization and discussion are stated. A standard framework according to CRISP-DM is additionally adapted to manage all activities. As a discussion, current issue and future trend are mentioned. The amount of the works published in this scope is substantial and it is impossible to discuss all of them on a single work. We hope this paper will make it possible to explore previous works and identify interesting areas for future research.
•This paper reviews data mining application in medicine from 1999 to 2013.•Medical data mining definition and its main goals and challenges are clarified.•Comprehensive categorization is presented to complete previous works and integrate medical and data mining issues.•Each paper is studied from six medical tasks and in each tasks, five data mining approaches are considered.•A discussion deals with previous research outline and future trend. Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction. Data mining is supposed to be able to handle various data types in all formats. Relevance of this paper is emphasized by the fact that data mining is an object of research in different areas. In this paper, we review previous works in the context of knowledge extraction from medical data. The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners. Medical data mining is a multidisciplinary field with contribution of medicine and data mining. Due to this fact, previous works should be classified to cover all users’ requirements from various fields. Because of this, we have studied papers with the aim of extracting knowledge from structural medical data published between 1999 and 2013. We clarify medical data mining and its main goals. Therefore, each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management. In each task, five data mining approaches are considered: classification, regression, clustering, association and hybrid. At the end of each task, a brief summarization and discussion are stated. A standard framework according to CRISP-DM is additionally adapted to manage all activities. As a discussion, current issue and future trend are mentioned. The amount of the works published in this scope is substantial and it is impossible to discuss all of them on a single work. We hope this paper will make it possible to explore previous works and identify interesting areas for future research.
Author Moghadam, Amir-Masoud Eftekhari
Tabar, Vahid Kashani
Babavalian, Mohammad Reza
Esfandiari, Nura
Author_xml – sequence: 1
  givenname: Nura
  surname: Esfandiari
  fullname: Esfandiari, Nura
  email: n.esfandiary@qiau.ac.ir
  organization: Faculty of Computer and Information Technology, Qazvin Branch, Islamic Azad University, Qazvin, Iran
– sequence: 2
  givenname: Mohammad Reza
  surname: Babavalian
  fullname: Babavalian, Mohammad Reza
  email: m.babavalian@qiau.ac.ir
  organization: Faculty of Computer and Information Technology, Qazvin Branch, Islamic Azad University, Qazvin, Iran
– sequence: 3
  givenname: Amir-Masoud Eftekhari
  surname: Moghadam
  fullname: Moghadam, Amir-Masoud Eftekhari
  email: eftekhari@qiau.ac.ir
  organization: Faculty of Computer and Information Technology, Qazvin Branch, Islamic Azad University, Qazvin, Iran
– sequence: 4
  givenname: Vahid Kashani
  surname: Tabar
  fullname: Tabar, Vahid Kashani
  email: vahidkashanitabar@gmail.com
  organization: Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran
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Issue 9
Keywords Medicine
Data mining algorithms
Medical data mining
Disease
Data mining application
Medical data
Knowledge engineering
Data type
Prognosis
Information extraction
Data mining
Relevance
Biomedical data processing
Classification
Knowledge discovery
Diagnosis
Medical application
Monitoring
Multidisciplinary
User need
Data analysis
Abstract
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Record format
Medical screening
Knowledge extraction
Standards
Surveillance
User requirement
Language English
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Huang (10.1016/j.eswa.2014.01.011_b0520) 2009; 93
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Subasi (10.1016/j.eswa.2014.01.011_b1290) 2012; 42
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Hautemanière (10.1016/j.eswa.2014.01.011_b0465) 2011; 39
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Pandey (10.1016/j.eswa.2014.01.011_b0965) 2009; 39
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Ozcift (10.1016/j.eswa.2014.01.011_b0945) 2012; 36
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Prati (10.1016/j.eswa.2014.01.011_b1075) 2011; 23
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Laxminarayan (10.1016/j.eswa.2014.01.011_b0705) 2006; 10
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Pietraszek (10.1016/j.eswa.2014.01.011_b1045) 2005; 10
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Lee (10.1016/j.eswa.2014.01.011_b0725) 2011; 1
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Kusiak (10.1016/j.eswa.2014.01.011_b0660) 2005; 35
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10.1016/j.eswa.2014.01.011_b0200
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SecondaryResourceType review_article
Snippet •This paper reviews data mining application in medicine from 1999 to 2013.•Medical data mining definition and its main goals and challenges are...
Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge...
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SubjectTerms Applied sciences
Biological and medical sciences
Computer science; control theory; systems
Computerized, statistical medical data processing and models in biomedicine
Data mining
Data mining algorithms
Data mining application
Data processing. List processing. Character string processing
Disease
Exact sciences and technology
Extraction
Medical
Medical data mining
Medical management aid. Diagnosis aid
Medical sciences
Medicine
Memory organisation. Data processing
Regression
Software
Tasks
Trends
Title Knowledge discovery in medicine: Current issue and future trend
URI https://dx.doi.org/10.1016/j.eswa.2014.01.011
https://www.proquest.com/docview/1551115348
https://www.proquest.com/docview/1677964910
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