Context-Aware Correction of Spelling Errors in Hungarian Medical Documents

Siklósi, Borbála; Novák, Attila; Prószéky, Gábor

doi:10.1007/978-3-642-39593-2_22

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…These results are comparable to those reported in similar studies on English and Hungarian patient records (Patrick et al, 2010;Siklósi et al, 2013).…”

Section: Discussionsupporting

confidence: 92%

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

Grigonytė

Kvist

Velupillai

et al. 2014

Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR)

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This paper describes part of an ongoing effort to improve the readability of Swedish electronic health records (EHRs). An EHR contains systematic documentation of a single patient's medical history across time, entered by healthcare professionals with the purpose of enabling safe and informed care. Linguistically, medical records exemplify a highly specialised domain, which can be superficially characterised as having telegraphic sentences involving displaced or missing words, abundant abbreviations, spelling variations including misspellings, and terminology. We report results on lexical simplification of Swedish EHRs, by which we mean detecting the unknown, out-ofdictionary words and trying to resolve them either as compounded known words, abbreviations or misspellings.

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“…These results are comparable to those reported in similar studies on English and Hungarian patient records (Patrick et al, 2010;Siklósi et al, 2013).…”

Section: Discussionsupporting

confidence: 92%

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

Grigonytė

Kvist

Velupillai

et al. 2014

Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR)

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“…However, these rules are exclusively applicable to English, so they could not be generalized to other languages. In recent years, studies have also been conducted on error patterns for other languages, namely Portuguese (Gimenes, Roman & Carvalho, 2015), Hungarian (Siklósi, Novák & Prószéky, 2016), Japanese (Baba & Suzuki, 2012), Danish (Paggio, 2000), and Punjabi (Lehal & Bhagat, 2007).…”

Section: Error Typologiesmentioning

confidence: 99%

Linguistic errors in the biomedical domain: Towards an error typology for Spanish

Hernández¹,

Almela²,

Valencia‐García³

2021

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Linguistic errors in the biomedical domain: Towards an error typology for Spanish. The objective of this work is the analysis of errors contained in a corpus of medical reports in natural language and the design of a typology of errors, as there was no systematic review on verification and correction of errors in clinical documentation in Spanish. In the development of automatic detection and correction systems, it is of great interest to delve into the nature of the linguistic errors that occur in clinical reports, in order to detect and treat them properly. The results show that omission errors are the most frequent ones in the analyzed sample, and that word length certainly influences error frequency. The typification of error patterns provided is enabling the development of a module based on linguistic knowledge, which is currently in progress. This will help to improve the performance of error detection and correction systems for the biomedical domain.

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“…The authors obtained 95% correction of spelling errors. Siklósi et al (2016) presented a context-aware system based on statistical machine translation (SMT) to correct spelling errors in Hungarian clinical text. The authors did not have access to any word list with correctly spelled clinical terms or to parallel corpora with correct and misspelled clinical text, so they used SMT as support to select possible correctly spelled candidate terms.…”

Section: Spell Checking Of Clinical Textmentioning

confidence: 99%

Clinical Text Mining

Dalianis

2018

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Context-Aware Correction of Spelling Errors in Hungarian Medical Documents

Cited by 12 publications

References 7 publications

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

Linguistic errors in the biomedical domain: Towards an error typology for Spanish

Clinical Text Mining

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