An open source software for fast grid-based data-mining in spatial epidemiology (FGBASE).
Identifieur interne : 000117 ( PubMed/Corpus ); précédent : 000116; suivant : 000118An open source software for fast grid-based data-mining in spatial epidemiology (FGBASE).
Auteurs : David M. Baker ; Alain-Jacques ValleronSource :
- International journal of health geographics [ 1476-072X ] ; 2014.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : France.
- diagnosis : Diabetes Mellitus, Type 1.
- epidemiology : Diabetes Mellitus, Type 1.
- methods : Data Mining.
- statistics & numerical data : Data Mining.
- Child, Cohort Studies, Geographic Mapping, Humans, Software, Time Factors.
Abstract
Examining whether disease cases are clustered in space is an important part of epidemiological research. Another important part of spatial epidemiology is testing whether patients suffering from a disease are more, or less, exposed to environmental factors of interest than adequately defined controls. Both approaches involve determining the number of cases and controls (or population at risk) in specific zones. For cluster searches, this often must be done for millions of different zones. Doing this by calculating distances can lead to very lengthy computations. In this work we discuss the computational advantages of geographical grid-based methods, and introduce an open source software (FGBASE) which we have created for this purpose.
DOI: 10.1186/1476-072X-13-46
PubMed: 25358866
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pubmed:25358866Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">An open source software for fast grid-based data-mining in spatial epidemiology (FGBASE).</title><author><name sortKey="Baker, David M" sort="Baker, David M" uniqKey="Baker D" first="David M" last="Baker">David M. Baker</name><affiliation><nlm:affiliation>Institut National de la Santé et de la Recherche Médicale (U986), Bicêtre Hospital, Paris-Sud University, Paris, France. david.baker@inserm.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Valleron, Alain Jacques" sort="Valleron, Alain Jacques" uniqKey="Valleron A" first="Alain-Jacques" last="Valleron">Alain-Jacques Valleron</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:25358866</idno><idno type="pmid">25358866</idno><idno type="doi">10.1186/1476-072X-13-46</idno><idno type="wicri:Area/PubMed/Corpus">000117</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">An open source software for fast grid-based data-mining in spatial epidemiology (FGBASE).</title><author><name sortKey="Baker, David M" sort="Baker, David M" uniqKey="Baker D" first="David M" last="Baker">David M. Baker</name><affiliation><nlm:affiliation>Institut National de la Santé et de la Recherche Médicale (U986), Bicêtre Hospital, Paris-Sud University, Paris, France. david.baker@inserm.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Valleron, Alain Jacques" sort="Valleron, Alain Jacques" uniqKey="Valleron A" first="Alain-Jacques" last="Valleron">Alain-Jacques Valleron</name></author></analytic><series><title level="j">International journal of health geographics</title><idno type="eISSN">1476-072X</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Child</term><term>Cohort Studies</term><term>Data Mining (methods)</term><term>Data Mining (statistics & numerical data)</term><term>Diabetes Mellitus, Type 1 (diagnosis)</term><term>Diabetes Mellitus, Type 1 (epidemiology)</term><term>France (epidemiology)</term><term>Geographic Mapping</term><term>Humans</term><term>Software</term><term>Time Factors</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>France</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Diabetes Mellitus, Type 1</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Diabetes Mellitus, Type 1</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Data Mining</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Data Mining</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Child</term><term>Cohort Studies</term><term>Geographic Mapping</term><term>Humans</term><term>Software</term><term>Time Factors</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Examining whether disease cases are clustered in space is an important part of epidemiological research. Another important part of spatial epidemiology is testing whether patients suffering from a disease are more, or less, exposed to environmental factors of interest than adequately defined controls. Both approaches involve determining the number of cases and controls (or population at risk) in specific zones. For cluster searches, this often must be done for millions of different zones. Doing this by calculating distances can lead to very lengthy computations. In this work we discuss the computational advantages of geographical grid-based methods, and introduce an open source software (FGBASE) which we have created for this purpose.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25358866</PMID><DateCreated><Year>2014</Year><Month>11</Month><Day>15</Day></DateCreated><DateCompleted><Year>2015</Year><Month>04</Month><Day>17</Day></DateCompleted><DateRevised><Year>2015</Year><Month>10</Month><Day>29</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1476-072X</ISSN><JournalIssue CitedMedium="Internet"><Volume>13</Volume><PubDate><Year>2014</Year><Month>Oct</Month><Day>30</Day></PubDate></JournalIssue><Title>International journal of health geographics</Title><ISOAbbreviation>Int J Health Geogr</ISOAbbreviation></Journal><ArticleTitle>An open source software for fast grid-based data-mining in spatial epidemiology (FGBASE).</ArticleTitle><Pagination><MedlinePgn>46</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1476-072X-13-46</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Examining whether disease cases are clustered in space is an important part of epidemiological research. Another important part of spatial epidemiology is testing whether patients suffering from a disease are more, or less, exposed to environmental factors of interest than adequately defined controls. Both approaches involve determining the number of cases and controls (or population at risk) in specific zones. For cluster searches, this often must be done for millions of different zones. Doing this by calculating distances can lead to very lengthy computations. In this work we discuss the computational advantages of geographical grid-based methods, and introduce an open source software (FGBASE) which we have created for this purpose.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Geographical grids based on the Lambert Azimuthal Equal Area projection are well suited for spatial epidemiology because they preserve area: each cell of the grid has the same area. We describe how data is projected onto such a grid, as well as grid-based algorithms for spatial epidemiological data-mining. The software program (FGBASE), that we have developed, implements these grid-based methods.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The grid based algorithms perform extremely fast. This is particularly the case for cluster searches. When applied to a cohort of French Type 1 Diabetes (T1D) patients, as an example, the grid based algorithms detected potential clusters in a few seconds on a modern laptop. This compares very favorably to an equivalent cluster search using distance calculations instead of a grid, which took over 4 hours on the same computer. In the case study we discovered 4 potential clusters of T1D cases near the cities of Le Havre, Dunkerque, Toulouse and Nantes. One example of environmental analysis with our software was to study whether a significant association could be found between distance to vineyards with heavy pesticide. None was found. In both examples, the software facilitates the rapid testing of hypotheses.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Grid-based algorithms for mining spatial epidemiological data provide advantages in terms of computational complexity thus improving the speed of computations. We believe that these methods and this software tool (FGBASE) will lower the computational barriers to entry for those performing epidemiological research.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Baker</LastName><ForeName>David M</ForeName><Initials>DM</Initials><AffiliationInfo><Affiliation>Institut National de la Santé et de la Recherche Médicale (U986), Bicêtre Hospital, Paris-Sud University, Paris, France. david.baker@inserm.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Valleron</LastName><ForeName>Alain-Jacques</ForeName><Initials>AJ</Initials></Author></AuthorList><Language>ENG</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016448">Multicenter Study</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>Oct</Month><Day>30</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Int J Health Geogr</MedlineTA><NlmUniqueID>101152198</NlmUniqueID><ISSNLinking>1476-072X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Epidemiology. 2002 Jul;13(4):373-5</RefSource><PMID Version="1">12094088</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Curr Opin Infect Dis. 2013 Jun;26(3):263-9</RefSource><PMID Version="1">23549392</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Stat Med. 2003 Jan 30;22(2):169-86</RefSource><PMID Version="1">12520555</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Stat Med. 1988 Jun;7(6):649-60</RefSource><PMID Version="1">3406597</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Epidemiol 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Version="1">19118289</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Lancet. 2009 Jun 13;373(9680):2027-33</RefSource><PMID Version="1">19481249</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>N Engl J Med. 2010 Nov 11;363(20):1900-8</RefSource><PMID Version="1">21067382</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Curr Opin Endocrinol Diabetes Obes. 2011 Aug;18(4):248-51</RefSource><PMID Version="1">21844707</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Scand J Public Health. 2012 Mar;40(2):177-82</RefSource><PMID Version="1">22312031</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Ann N Y Acad Sci. 2013 Apr;1281:1-15</RefSource><PMID Version="1">23360422</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Eur J Epidemiol. 2001;17(11):1023-7</RefSource><PMID Version="1">12380716</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015331" MajorTopicYN="N">Cohort Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D057225" MajorTopicYN="N">Data Mining</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003922" MajorTopicYN="N">Diabetes Mellitus, Type 1</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005602" MajorTopicYN="N" Type="Geographic">France</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062305" MajorTopicYN="Y">Geographic Mapping</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="Y">Software</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4233060</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>8</Month><Day>12</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>9</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>11</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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