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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F11%3A00186231%21RIV12-MZ0-21230___
rdf:type
skos:Concept n9:Vysledek
rdfs:seeAlso
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6089470
dcterms:description
In this work we have studied, evaluated and proposed different swarm intelligence techniques for mining information from loosely structured medical textual records with no apriori knowledge. In the paper we depict the process of mining a large dataset of ~50,000-120,000 records x 20 attributes in database tables, originating from the hospital information system (thanks go to the University Hospital in Brno, Czech Republic) recording over 10 years. This paper concerns only textual attributes with free text input, that means 613,000 text fields in 16 attributes. Each attribute item contains ~800-1,500 characters (diagnoses, medications, etc.). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining and automated processing. In this work we have studied, evaluated and proposed different swarm intelligence techniques for mining information from loosely structured medical textual records with no apriori knowledge. In the paper we depict the process of mining a large dataset of ~50,000-120,000 records x 20 attributes in database tables, originating from the hospital information system (thanks go to the University Hospital in Brno, Czech Republic) recording over 10 years. This paper concerns only textual attributes with free text input, that means 613,000 text fields in 16 attributes. Each attribute item contains ~800-1,500 characters (diagnoses, medications, etc.). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining and automated processing.
dcterms:title
Ant Inspired Techniques in Textual Information Retrieval from a Hospital Information System Ant Inspired Techniques in Textual Information Retrieval from a Hospital Information System
skos:prefLabel
Ant Inspired Techniques in Textual Information Retrieval from a Hospital Information System Ant Inspired Techniques in Textual Information Retrieval from a Hospital Information System
skos:notation
RIV/68407700:21230/11:00186231!RIV12-MZ0-21230___
n9:predkladatel
n10:orjk%3A21230
n3:aktivita
n7:Z n7:P
n3:aktivity
P(NT11124), Z(MSM6840770012)
n3:dodaniDat
n16:2012
n3:domaciTvurceVysledku
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n3:druhVysledku
n21:D
n3:duvernostUdaju
n6:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
186368
n3:idVysledku
RIV/68407700:21230/11:00186231
n3:jazykVysledku
n23:eng
n3:klicovaSlova
Information Retrieval; Ant Colony; Swarm Intelligence
n3:klicoveSlovo
n13:Ant%20Colony n13:Swarm%20Intelligence n13:Information%20Retrieval
n3:kontrolniKodProRIV
[48DF2D078DC0]
n3:mistoKonaniAkce
Salamanca
n3:mistoVydani
New York
n3:nazevZdroje
Proceedings of the 2011 Third World Congress on Nature and Biologically Inspired Computing
n3:obor
n17:JC
n3:pocetDomacichTvurcuVysledku
5
n3:pocetTvurcuVysledku
7
n3:projekt
n24:NT11124
n3:rokUplatneniVysledku
n16:2011
n3:tvurceVysledku
Janků, P. Spilka, Jiří Lhotská, Lenka Huser, M. Chudáček, Václav Huptych, Michal Burša, Miroslav
n3:typAkce
n25:WRD
n3:zahajeniAkce
2011-10-19+02:00
n3:zamer
n15:MSM6840770012
s:numberOfPages
6
n22:doi
10.1109/NaBIC.2011.6089470
n20:hasPublisher
IEEE - Systems, Man, and Cybernetics Society
n12:isbn
978-1-4577-1123-7
n8:organizacniJednotka
21230