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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FD - Onkologie a hematologie

OECD FORD obor

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Projekt

<a href="/cs/project/NT11124" target="_blank" >NT11124: Vliv hodnocení kardiotokografie pomocí metod umělé inteligence na kvalitu perinatální péče</a><br>

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF APPLIED LOGIC

ISSN

1570-8683

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

2 SI

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

126-137

Kód UT WoS článku

000350924200005

EID výsledku v databázi Scopus

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