Information retrieval from hospital information system: Increasing effectivity using swarm intelligence

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F15%3A00063038" target="_blank" >RIV/65269705:_____/15:00063038 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/15:00225922 RIV/00216224:14110/15:00082475

Result on the web

<a href="http://www.sciencedirect.com/science/article/pii/S1570868314000809" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1570868314000809</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jal.2014.11.006" target="_blank" >10.1016/j.jal.2014.11.006</a>

Result language

angličtina

Original language name

Information retrieval from hospital information system: Increasing effectivity using swarm intelligence

Original language description

This paper details the process of mining information from a hospital information system that has been designed approximately 15 years ago. The information is distributed within database tables in large textual attributes with a free structure. Information retrieval from these information is necessary for complementing cardiotocography signals with additional information that is to be implemented in a decision support system. The basic statistical overview (n-gram analysis) helped with the insight into data structure, however more sophisticated methods have to be used as human (and expert) processing of the whole data were out of consideration: over 620,000 text fields contained text reports in natural language with (many) typographical errors, duplicates, ambiguities, syntax errors and many (nonstandard) abbreviations. There was a strong need to efficiently determine the overall structure of the database and discover information that is important from the clinical point of view. We have used three different methods: k-means, self-organizing map and a self-organizing approach inspired by ant-colonies that performed clustering of the records. The records were visualized and revealed the most prominent information structure(s) that were consulted with medical experts and served for further mining from the database. The outcome of this task is a set of ordered or nominal attributes with a structural information that is available for rule discovery mining and automated processing for the research of asphyxia prediction during delivery. The proposed methodology has significantly reduced the processing time of loosely structured textual records for both IT and medical experts.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

FD - Oncology and haematology

OECD FORD branch

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Project

<a href="/en/project/NT11124" target="_blank" >NT11124: Impact of Cardiotocography evaluation by means of artificial inteligence on perinatal care</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

JOURNAL OF APPLIED LOGIC

ISSN

1570-8683

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

2 SI

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

126-137

UT code for WoS article

000350924200005

EID of the result in the Scopus database

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