Publikation: Medical social media analytics via ranking and big learning : an image-based disease prediction study
Lade...
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2014
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
IEEE, , ed.. Proceedings 2014 : IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) ; October 18-19 , 2014 Wuhan, Hubei, China. IEEE, 2014, pp. 389-394. ISBN 978-1-4799-5352-3. Available under: doi: 10.1109/SPAC.2014.6982722
Zusammenfassung
Medical social media analytics becomes more and more popular nowadays because of its effectiveness in benefiting diverse health-care applications. In this study, the essential disease prediction task is investigated and realized via medical social media analytics techniques. To be specific, arterial spin labeling (ASL), an emerging functional magnetic resonance imaging modality, is utilized to provide image-based information and novel ranking as well as learning techniques are proposed and incorporated to fulfill the disease prediction task in dementia. To demonstrate its superiority, comprehensive statistical experiments are conducted with comparison to several conventional methods. Promising results are reported from this study.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 18. Okt. 2014 - 19. Okt. 2014, Wuhan
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690
HUANG, Wei, Peng ZHANG, Minmin SHEN, 2014. Medical social media analytics via ranking and big learning : an image-based disease prediction study. IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). Wuhan, 18. Okt. 2014 - 19. Okt. 2014. In: IEEE, , ed.. Proceedings 2014 : IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) ; October 18-19 , 2014 Wuhan, Hubei, China. IEEE, 2014, pp. 389-394. ISBN 978-1-4799-5352-3. Available under: doi: 10.1109/SPAC.2014.6982722BibTex
@inproceedings{Huang2014Medic-30286,
year={2014},
doi={10.1109/SPAC.2014.6982722},
title={Medical social media analytics via ranking and big learning : an image-based disease prediction study},
isbn={978-1-4799-5352-3},
publisher={IEEE},
booktitle={Proceedings 2014 : IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) ; October 18-19 , 2014 Wuhan, Hubei, China},
pages={389--394},
editor={IEEE},
author={Huang, Wei and Zhang, Peng and Shen, Minmin}
}RDF
<rdf:RDF
xmlns:dcterms="http://purl.org/dc/terms/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:bibo="http://purl.org/ontology/bibo/"
xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:void="http://rdfs.org/ns/void#"
xmlns:xsd="http://www.w3.org/2001/XMLSchema#" >
<rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/30286">
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
<void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
<dcterms:abstract xml:lang="eng">Medical social media analytics becomes more and more popular nowadays because of its effectiveness in benefiting diverse health-care applications. In this study, the essential disease prediction task is investigated and realized via medical social media analytics techniques. To be specific, arterial spin labeling (ASL), an emerging functional magnetic resonance imaging modality, is utilized to provide image-based information and novel ranking as well as learning techniques are proposed and incorporated to fulfill the disease prediction task in dementia. To demonstrate its superiority, comprehensive statistical experiments are conducted with comparison to several conventional methods. Promising results are reported from this study.</dcterms:abstract>
<dc:contributor>Shen, Minmin</dc:contributor>
<dc:contributor>Zhang, Peng</dc:contributor>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52"/>
<dc:creator>Zhang, Peng</dc:creator>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
<dcterms:title>Medical social media analytics via ranking and big learning : an image-based disease prediction study</dcterms:title>
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52"/>
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-16T10:02:15Z</dc:date>
<dc:contributor>Huang, Wei</dc:contributor>
<dcterms:issued>2014</dcterms:issued>
<dc:creator>Shen, Minmin</dc:creator>
<foaf:homepage rdf:resource="http://localhost:8080/"/>
<bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/30286"/>
<dc:language>eng</dc:language>
<dc:creator>Huang, Wei</dc:creator>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-16T10:02:15Z</dcterms:available>
</rdf:Description>
</rdf:RDF>Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
