Professional language in Swedish clinical text: Linguistic characterization and comparative studies

Smith, K. L.; Megyesi, Beáta; Velupillai, Sumithra; Kvist, Maria

doi:10.1017/s0332586514000213

Cited by 11 publications

(12 citation statements)

References 16 publications

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“…Characteristics of clinical text are surprisingly similar in different even unrelated languages (Friedman et al 2002, Surján & Héja 2003, Laippala et al 2009, Hagège et al 2011, Bretschneider, Zillner & Hammon 2013, Temnikova et al 2013, Smith et al 2014). Several of these characteristics reflect the constant time pressure in healthcare, such as telegraphic text omitting words and frequent use of ad hoc abbreviations.…”

Section: Introductionmentioning

confidence: 99%

Swedification patterns of Latin and Greek affixes in clinical text

et al. 2016

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Swedish medical language is rich with Latin and Greek terminology which has undergone a Swedification since the 1980s. However, many original expressions are still used by clinical professionals. The goal of this study is to obtain precise quantitative measures of how the foreign terminology is manifested in Swedish clinical text. To this end, we explore the use of Latin and Greek affixes in Swedish medical texts in three genres: clinical text, scientific medical text and online medical information for laypersons. More specifically, we use frequency lists derived from tokenised Swedish medical corpora in the three domains, and extract word pairs belonging to types that display both the original and Swedified spellings. We describe six distinct patterns explaining the variation in the usage of Latin and Greek affixes in clinical text. The results show that to a large extent affixes in clinical text are Swedified and that prefixes are used more conservatively than suffixes.

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Section: Introductionmentioning

confidence: 99%

Swedification patterns of Latin and Greek affixes in clinical text

et al. 2016

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“…The used medical corpus has the advantage of being freely available, in contrast to large clinical corpora, which are only rarely available for research, and it also makes it possible for anyone to repeat the experiments carried out in this study. As there are many differences between medical journal text and clinical text (Smith et al, 2014), some marker words might be used in other contexts in clinical text than in medical journal text, Figure 3: The vocabulary used for the experiments, displayed in a font size corresponding to how often a word, when included in the evaluation data, was retrieved among the top 1,000 candidates. Words displayed in black were retrieved in less than 10% of the times they were included in the evaluation data.…”

Section: Resultsmentioning

confidence: 99%

Expanding a dictionary of marker words for uncertainty and negation using distributional semantics

Alfalahi¹,

Skeppstedt

Ahlbom³

et al. 2015

Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

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Approaches to determining the factuality of diagnoses and findings in clinical text tend to rely on dictionaries of marker words for uncertainty and negation. Here, a method for semi-automatically expanding a dictionary of marker words using distributional semantics is presented and evaluated. It is shown that ranking candidates for inclusion according to their proximity to cluster centroids of semantically similar seed words is more successful than ranking them according to proximity to each individual seed word.

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“…In online medical guidance, the narrative clinical texts written by patients often contain typos, misspellings, abbreviations, non-standard jargons, as well as incomplete sentences [6]. The oral expression of medical terms is therefore difficult to be processed by natural language processing (NLP) tools developed for ordinary text [7]. To address these issues, we investigate an automated regular expression generation method to classify medical texts in order to provide informative and comprehensive human-like medical guidance.…”

Section: Introductionmentioning

confidence: 99%

Regular Expression Based Medical Text Classification Using Constructive Heuristic Approach

Cui

Bai

et al. 2019

IEEE Access

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Medical text classification assigns medical related text into different categories such as topics or disease types. Machine learning based techniques have been widely used to perform such tasks despite the obvious drawback in such ''black box'' approach, leaving no easy way to fine-tune the resultant model for better performance. We propose a novel constructive heuristic approach to generate a set of regular expressions that can be used as effective text classifiers. The main innovation of our approach is that we develop a novel regular expression based text classifier with both satisfactory classification performance and excellent interpretability. We evaluate our framework on real-world medical data provided by our collaborator, one of the largest online healthcare providers in the market, and observe the high performance and consistency of this approach. Experimental results show that the machine-generated regular expressions can be effectively used in conjunction with machine learning techniques to perform medical text classification tasks. The proposed methodology improves the performance of baseline methods (Naive Bayes and Support Vector Machines) by 9% in precision and 4.5% in recall. We also evaluate the performance of modified regular expressions by human experts and demonstrate the potential of practical applications using the proposed method. INDEX TERMS Regular expressions, text classification, constructive heuristic method.

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Professional language in Swedish clinical text: Linguistic characterization and comparative studies

Cited by 11 publications

References 16 publications

Swedification patterns of Latin and Greek affixes in clinical text

Swedification patterns of Latin and Greek affixes in clinical text

Expanding a dictionary of marker words for uncertainty and negation using distributional semantics

Regular Expression Based Medical Text Classification Using Constructive Heuristic Approach

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