Named Entity Recognition and Classification for Medical Prospectuses.
Structuring and processing natural language is a growing challenge in the medical field. Researchers are looking for new ways to extract knowledge to create databases and applications to help doctors treat patients and minimize medical errors. A very important part in treating a patient is to provid...
| Publicado en: | Studies in Health Technology & Informatics Vol. 262; pp. 284 - 288 |
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| Autores principales: | , , |
| Formato: | computer program pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137369845&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137369845 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2019 vid: 262 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 137369845 137369845 137369845 10.3233/SHTI190074 137369845 ppf: 284 ppct: 4 formats: tig: atl: Named Entity Recognition and Classification for Medical Prospectuses. aug: au: CHIRILA, Oana Sorina CHIRILA, Ciprian-Bogdan STOICU-TIVADAR, Lăcrămioara affil: Department of Automation and Applied Informatics, University Politehnica Timişoara, Timişoara, Timiş, Romania sug: subj: Decision Support Systems, Clinical Database Construction Natural Language Processing Romania Language Database Management Software Software Design Information Retrieval Medical Informatics Algorithms ab: Structuring and processing natural language is a growing challenge in the medical field. Researchers are looking for new ways to extract knowledge to create databases and applications to help doctors treat patients and minimize medical errors. A very important part in treating a patient is to provide a fair and effective treatment for diseases. In this article we present a method of extracting important information from medical prospectuses, such as a drug-treated condition, a medicine name, a drug type, etc. To extract these entities, we use Stanford NER Tagger trained for prospectuses in Romanian language. The model was trained and tested with 3 types of medication. For each test, the accuracy of the extracted data was calculated. The extracted medical information is used to create databases with structured information that are useful for decision-support applications to check for or find suggestions for the best treatments. pubtype: Academic Journal doctype: computer program pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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