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| rdf:type
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| rdfs:seeAlso
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| Description
| - This paper describes the process of 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. 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. medical terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities and many (nonstandard) abbreviations. 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.
- This paper describes the process of 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. 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. medical terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities and many (nonstandard) abbreviations. 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
| - Obstetric Medical Record Processing and Information Retrieval
- Obstetric Medical Record Processing and Information Retrieval (en)
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| skos:prefLabel
| - Obstetric Medical Record Processing and Information Retrieval
- Obstetric Medical Record Processing and Information Retrieval (en)
|
| skos:notation
| - RIV/68407700:21230/12:00201789!RIV13-MSM-21230___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
| - P(NT11124), Z(MSM6840770012)
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/12:00201789
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| http://linked.open...riv/jazykVysledku
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| 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
| |
| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/mistoVydani
| |
| http://linked.open...vEdiceCisloSvazku
| |
| http://linked.open...i/riv/nazevZdroje
| |
| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...v/pocetStranKnihy
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Burša, Miroslav
- Chudáček, Václav
- Huptych, Michal
- Huser, M.
- Janku, P.
- Lhotská, Lenka
- Spilka, Jiří
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| http://linked.open...n/vavai/riv/zamer
| |
| number of pages
| |
| http://bibframe.org/vocab/doi
| - 10.1007/978-3-642-29262-0_4
|
| http://purl.org/ne...btex#hasPublisher
| |
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
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