Statistical semantic and clinician confidence analysis for correcting abbreviations and spelling errors in clinical progress notes

Wong, Wilson; Glance, David

doi:10.1016/j.artmed.2011.08.003

Cited by 18 publications

(18 citation statements)

References 9 publications

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“…In the last two decades, spelling correction methods for clinical texts have been studied extensively. Nevertheless, the majority of related studies mainly focused on the electronic medical record (EMR) [ 1 ], but largely ignored consumer-generated content which has accumulated rapidly because of the development of online media and social networks. The consumers mentioned here include those who describe their symptoms and seek online medical assistance, and those who have been successfully cured and willing to share their treatment process experience on public websites or forums.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous approaches for correcting spelling errors, such as Levenshtein edit distance [ 4 , 5 ] and semantic correction [ 1 , 6 ], have been proposed. The Levenshtein edit distance model demonstrates a method to measure the edit distance of converting one string to another, which is calculated by counting the number of four-letter operations—deletions, insertions, transpositions, and substitutions—during the conversion.…”

Section: Introductionmentioning

confidence: 99%

“…The semantic correction model utilizes context-sensitive detection and has been widely applied to studies using natural language processing (NLP). For example, Wong and Glance [ 1 ] developed a robust system using semantic correction to correct misspelled words, especially abbreviation disambiguation, in progress notes. In addition, according to a study proposed by Ruch et al [ 7 ], these two models can be combined: first, the Levenshtein edit distance is computed and the resulting candidate words are ranked according to the edit distance.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies applied an integrated spelling correction application programming interface (API), such as GNU Aspell, Yahoo API, etc. Wong and Glance [ 1 ] adopted and mixed GNU Aspell and Yahoo API corrective interfaces in their systems for real-time abbreviation disambiguation, which has achieved good results. These interfaces have become highly sound and mature after a long period of development.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, most of the prevalent evaluation methods for spelling correction are based on manual inspection [ 1 ]. Although it is accurate, the manual evaluation is time consuming, and not feasible to be applied in large-scale experiments.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care

Zhou¹,

Zheng²,

Yin³

et al. 2015

JMIR Med Inform

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BackgroundConsumer-generated content, such as postings on social media websites, can serve as an ideal source of information for studying health care from a consumer’s perspective. However, consumer-generated content on health care topics often contains spelling errors, which, if not corrected, will be obstacles for downstream computer-based text analysis.ObjectiveIn this study, we proposed a framework with a spelling correction system designed for consumer-generated content and a novel ontology-based evaluation system which was used to efficiently assess the correction quality. Additionally, we emphasized the importance of context sensitivity in the correction process, and demonstrated why correction methods designed for electronic medical records (EMRs) failed to perform well with consumer-generated content.MethodsFirst, we developed our spelling correction system based on Google Spell Checker. The system processed postings acquired from MedHelp, a biomedical bulletin board system (BBS), and saved misspelled words (eg, sertaline) and corresponding corrected words (eg, sertraline) into two separate sets. Second, to reduce the number of words needing manual examination in the evaluation process, we respectively matched the words in the two sets with terms in two biomedical ontologies: RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT). The ratio of words which could be matched and appropriately corrected was used to evaluate the correction system’s overall performance. Third, we categorized the misspelled words according to the types of spelling errors. Finally, we calculated the ratio of abbreviations in the postings, which remarkably differed between EMRs and consumer-generated content and could largely influence the overall performance of spelling checkers.ResultsAn uncorrected word and the corresponding corrected word was called a spelling pair, and the two words in the spelling pair were its members. In our study, there were 271 spelling pairs detected, among which 58 (21.4%) pairs had one or two members matched in the selected ontologies. The ratio of appropriate correction in the 271 overall spelling errors was 85.2% (231/271). The ratio of that in the 58 spelling pairs was 86% (50/58), close to the overall ratio. We also found that linguistic errors took up 31.4% (85/271) of all errors detected, and only 0.98% (210/21,358) of words in the postings were abbreviations, which was much lower than the ratio in the EMRs (33.6%).ConclusionsWe conclude that our system can accurately correct spelling errors in consumer-generated content. Context sensitivity is indispensable in the correction process. Additionally, it can be confirmed that consumer-generated content differs from EMRs in that consumers seldom use abbreviations. Also, the evaluation method, taking advantage of biomedical ontology, can effectively estimate the accuracy of the correction system and reduce manual examination time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%