Toward unsupervised outbreak detection through visual perception of new patterns.
Identifieur interne : 000488 ( Main/Corpus ); précédent : 000487; suivant : 000489Toward unsupervised outbreak detection through visual perception of new patterns.
Auteurs : Pierre P. Lévy ; Alain-Jacques ValleronSource :
- BMC public health [ 1471-2458 ] ; 2009.
English descriptors
- KwdEn :
- Databases, Factual (MeSH), Disease (classification), Disease Outbreaks (MeSH), Emergency Service, Hospital (MeSH), Family Practice (MeSH), Female (MeSH), France (epidemiology), Humans (MeSH), Male (MeSH), Natural Language Processing (MeSH), Pattern Recognition, Visual (MeSH), Population Surveillance (methods), Retrospective Studies (MeSH), Sentinel Surveillance (MeSH), Vocabulary, Controlled (MeSH).
- MESH :
- geographic , epidemiology : France.
- classification : Disease.
- methods : Population Surveillance.
- Databases, Factual, Disease Outbreaks, Emergency Service, Hospital, Family Practice, Female, Humans, Male, Natural Language Processing, Pattern Recognition, Visual, Retrospective Studies, Sentinel Surveillance, Vocabulary, Controlled.
Abstract
BACKGROUND
Statistical algorithms are routinely used to detect outbreaks of well-defined syndromes, such as influenza-like illness. These methods cannot be applied to the detection of emerging diseases for which no preexisting information is available.This paper presents a method aimed at facilitating the detection of outbreaks, when there is no a priori knowledge of the clinical presentation of cases.
METHODS
The method uses a visual representation of the symptoms and diseases coded during a patient consultation according to the International Classification of Primary Care 2nd version (ICPC-2). The surveillance data are transformed into color-coded cells, ranging from white to red, reflecting the increasing frequency of observed signs. They are placed in a graphic reference frame mimicking body anatomy. Simple visual observation of color-change patterns over time, concerning a single code or a combination of codes, enables detection in the setting of interest.
RESULTS
The method is demonstrated through retrospective analyses of two data sets: description of the patients referred to the hospital by their general practitioners (GPs) participating in the French Sentinel Network and description of patients directly consulting at a hospital emergency department (HED).Informative image color-change alert patterns emerged in both cases: the health consequences of the August 2003 heat wave were visualized with GPs' data (but passed unnoticed with conventional surveillance systems), and the flu epidemics, which are routinely detected by standard statistical techniques, were recognized visually with HED data.
CONCLUSION
Using human visual pattern-recognition capacities to detect the onset of unexpected health events implies a convenient image representation of epidemiological surveillance and well-trained "epidemiology watchers". Once these two conditions are met, one could imagine that the epidemiology watchers could signal epidemiological alerts, based on "image walls" presenting the local, regional and/or national surveillance patterns, with specialized field epidemiologists assigned to validate the signals detected.
DOI: 10.1186/1471-2458-9-179
PubMed: 19515246
PubMed Central: PMC2706246
Links to Exploration step
pubmed:19515246Le document en format XML
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Lévy</name><affiliation><nlm:affiliation>Assistance Publique-Hôpitaux de Paris, Hôpital Tenon, Département de Santé Publique, Paris, France. levy@u707.jussieu.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="2009">2009</date><idno type="RBID">pubmed:19515246</idno><idno type="pmid">19515246</idno><idno type="doi">10.1186/1471-2458-9-179</idno><idno type="pmc">PMC2706246</idno><idno type="wicri:Area/Main/Corpus">000488</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000488</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Toward unsupervised outbreak detection through visual perception of new patterns.</title><author><name sortKey="Levy, Pierre P" sort="Levy, Pierre P" uniqKey="Levy P" first="Pierre P" last="Lévy">Pierre P. Lévy</name><affiliation><nlm:affiliation>Assistance Publique-Hôpitaux de Paris, Hôpital Tenon, Département de Santé Publique, Paris, France. levy@u707.jussieu.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">BMC public health</title><idno type="eISSN">1471-2458</idno><imprint><date when="2009" type="published">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Databases, Factual (MeSH)</term><term>Disease (classification)</term><term>Disease Outbreaks (MeSH)</term><term>Emergency Service, Hospital (MeSH)</term><term>Family Practice (MeSH)</term><term>Female (MeSH)</term><term>France (epidemiology)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Natural Language Processing (MeSH)</term><term>Pattern Recognition, Visual (MeSH)</term><term>Population Surveillance (methods)</term><term>Retrospective Studies (MeSH)</term><term>Sentinel Surveillance (MeSH)</term><term>Vocabulary, Controlled (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>France</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Disease</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Databases, Factual</term><term>Disease Outbreaks</term><term>Emergency Service, Hospital</term><term>Family Practice</term><term>Female</term><term>Humans</term><term>Male</term><term>Natural Language Processing</term><term>Pattern Recognition, Visual</term><term>Retrospective Studies</term><term>Sentinel Surveillance</term><term>Vocabulary, Controlled</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Statistical algorithms are routinely used to detect outbreaks of well-defined syndromes, such as influenza-like illness. These methods cannot be applied to the detection of emerging diseases for which no preexisting information is available.This paper presents a method aimed at facilitating the detection of outbreaks, when there is no a priori knowledge of the clinical presentation of cases.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>The method uses a visual representation of the symptoms and diseases coded during a patient consultation according to the International Classification of Primary Care 2nd version (ICPC-2). The surveillance data are transformed into color-coded cells, ranging from white to red, reflecting the increasing frequency of observed signs. They are placed in a graphic reference frame mimicking body anatomy. Simple visual observation of color-change patterns over time, concerning a single code or a combination of codes, enables detection in the setting of interest.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The method is demonstrated through retrospective analyses of two data sets: description of the patients referred to the hospital by their general practitioners (GPs) participating in the French Sentinel Network and description of patients directly consulting at a hospital emergency department (HED).Informative image color-change alert patterns emerged in both cases: the health consequences of the August 2003 heat wave were visualized with GPs' data (but passed unnoticed with conventional surveillance systems), and the flu epidemics, which are routinely detected by standard statistical techniques, were recognized visually with HED data.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>Using human visual pattern-recognition capacities to detect the onset of unexpected health events implies a convenient image representation of epidemiological surveillance and well-trained "epidemiology watchers". Once these two conditions are met, one could imagine that the epidemiology watchers could signal epidemiological alerts, based on "image walls" presenting the local, regional and/or national surveillance patterns, with specialized field epidemiologists assigned to validate the signals detected.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">19515246</PMID><DateCompleted><Year>2009</Year><Month>09</Month><Day>28</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2458</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><PubDate><Year>2009</Year><Month>Jun</Month><Day>10</Day></PubDate></JournalIssue><Title>BMC public health</Title><ISOAbbreviation>BMC Public Health</ISOAbbreviation></Journal><ArticleTitle>Toward unsupervised outbreak detection through visual perception of new patterns.</ArticleTitle><Pagination><MedlinePgn>179</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2458-9-179</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Statistical algorithms are routinely used to detect outbreaks of well-defined syndromes, such as influenza-like illness. These methods cannot be applied to the detection of emerging diseases for which no preexisting information is available.This paper presents a method aimed at facilitating the detection of outbreaks, when there is no a priori knowledge of the clinical presentation of cases.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">The method uses a visual representation of the symptoms and diseases coded during a patient consultation according to the International Classification of Primary Care 2nd version (ICPC-2). The surveillance data are transformed into color-coded cells, ranging from white to red, reflecting the increasing frequency of observed signs. They are placed in a graphic reference frame mimicking body anatomy. Simple visual observation of color-change patterns over time, concerning a single code or a combination of codes, enables detection in the setting of interest.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The method is demonstrated through retrospective analyses of two data sets: description of the patients referred to the hospital by their general practitioners (GPs) participating in the French Sentinel Network and description of patients directly consulting at a hospital emergency department (HED).Informative image color-change alert patterns emerged in both cases: the health consequences of the August 2003 heat wave were visualized with GPs' data (but passed unnoticed with conventional surveillance systems), and the flu epidemics, which are routinely detected by standard statistical techniques, were recognized visually with HED data.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">Using human visual pattern-recognition capacities to detect the onset of unexpected health events implies a convenient image representation of epidemiological surveillance and well-trained "epidemiology watchers". 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