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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.Doctors often use natural language in medical records.Therefore it contains many ambiguities due to non-standard abbreviations and synonyms. Themedical environment itself is also very specific: the natural langure 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, syntax errors and many nonstandard abbreviations. This paper describes the process of mining information from loosely structured medical textual records with no apriori knowledge. The paper concerns mining a large dataset of ~50,000-140,000 records x 20 attributes in relational database tables, originating from the hospitál information system (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 650,000 text fields in 16 attributes. Each attribute item contains approximately 800-1,500 characters (diagnoses, medications, anamneses, etc.). The output of this task is a set of ordered/nominal attributes suitable for 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.
- Information mining from textual data becomes a very challenging task when the structure of the text record is very loose without any rules.Doctors often use natural language in medical records.Therefore it contains many ambiguities due to non-standard abbreviations and synonyms. Themedical environment itself is also very specific: the natural langure 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, syntax errors and many nonstandard abbreviations. This paper describes the process of mining information from loosely structured medical textual records with no apriori knowledge. The paper concerns mining a large dataset of ~50,000-140,000 records x 20 attributes in relational database tables, originating from the hospitál information system (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 650,000 text fields in 16 attributes. Each attribute item contains approximately 800-1,500 characters (diagnoses, medications, anamneses, etc.). The output of this task is a set of ordered/nominal attributes suitable for 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. (en)
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| Title
| - Practical Problems and Solutions in Hospital Information System Data Mining
- Practical Problems and Solutions in Hospital Information System Data Mining (en)
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| skos:prefLabel
| - Practical Problems and Solutions in Hospital Information System Data Mining
- Practical Problems and Solutions in Hospital Information System Data Mining (en)
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| skos:notation
| - RIV/68407700:21230/12:00194641!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
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| 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/68407700:21230/12:00194641
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Swarm Intelligence; Ant Colony; Text Data Mining; Information Retrieval; 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
| - Information Technology in Bio- and Medical Informatics
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| 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.
- Janku, P.
- Lhotská, Lenka
- Spilka, Jiří
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
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| http://linked.open...n/vavai/riv/zamer
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| issn
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| number of pages
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| http://purl.org/ne...btex#hasPublisher
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| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
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