| Attributes | Values |
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| rdf:type
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| Description
| - Information mining from textual data becomes a very challenging task when the structure of the text record is very loose without any rules. The task becomes even more difficult when natural language is used and no apriori knowledge is available. The medical environment itself is also very specific: the natural language used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e. medici terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities, syntax errors and many (nonstandard) abbreviations. This paper describes the process of mining informatik 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 systém (thanks go to the University Hospital in Brno, Czech Republic) recording over 11 years. This paper concerns only textual attributes with free text input, that means 620,000 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 that can help in asphyxia prediction during delivery. The proposed technique has an important impact on reduction of the processing time of loosely structured textual records for experts. Note that this project is an ongoing process (and research) and new data are irregularly received from the medical facility, justifying the need for robust and fool-proof algorithms.
- Information mining from textual data becomes a very challenging task when the structure of the text record is very loose without any rules. The task becomes even more difficult when natural language is used and no apriori knowledge is available. The medical environment itself is also very specific: the natural language used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e. medici terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities, syntax errors and many (nonstandard) abbreviations. This paper describes the process of mining informatik 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 systém (thanks go to the University Hospital in Brno, Czech Republic) recording over 11 years. This paper concerns only textual attributes with free text input, that means 620,000 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 that can help in asphyxia prediction during delivery. The proposed technique has an important impact on reduction of the processing time of loosely structured textual records for experts. Note that this project is an ongoing process (and research) and new data are irregularly received from the medical facility, justifying the need for robust and fool-proof algorithms. (en)
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| Title
| - Effective Free-Text Medical Record Processing and Information Retrieval
- Effective Free-Text Medical Record Processing and Information Retrieval (en)
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| skos:prefLabel
| - Effective Free-Text Medical Record Processing and Information Retrieval
- Effective Free-Text Medical Record Processing and Information Retrieval (en)
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| skos:notation
| - RIV/68407700:21230/12:00193461!RIV13-MSM-21230___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
| - I, P(NT11124), Z(MSM6840770012)
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| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/12:00193461
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| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - Swarm Intelligence; Ant Colony, Textual Data Mining; Medical Record Processing; Hospital Information System (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - IFMBE Proceedings: World Congress on Medical Physics and Biomedical Engineering
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Burša, Miroslav
- Chudáček, Václav
- Huser, M.
- Janků, P.
- Lhotská, Lenka
- Spilka, Jiří
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
| |
| http://linked.open...n/vavai/riv/zamer
| |
| issn
| |
| number of pages
| |
| http://bibframe.org/vocab/doi
| - 10.1007/978-3-642-29305-4_342
|
| http://purl.org/ne...btex#hasPublisher
| |
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
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