Swedification patterns of Latin and Greek affixes in clinical text

2016 ◽  
Vol 39 (1) ◽  
pp. 5-37 ◽  
Author(s):  
Gintarė Grigonytė ◽  
Maria Kvist ◽  
Mats Wirén ◽  
Sumithra Velupillai ◽  
Aron Henriksson

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.

2021 ◽  
Vol 53 (1-2) ◽  
pp. 56-99
Author(s):  
Oh Chaekun ◽  
Jeon Jongwook ◽  
Kim Sanghyun ◽  
Yi Kiebok ◽  
Shin Dong-won

Abstract Prescriptions of Local Botanicals for Emergency Use (K. Hyang’yak Kugŭppang 鄕藥救急方) is the oldest medical text extant on the Korean Peninsula and known to have been compiled during the latter half of the Koryŏ 高麗 dynasty (918–1392 ce). The key value of this work lies in the dissemination and praxis of medical knowledge. First, the author used annotations in order to record Koryŏ people’s pronunciations of the names of medicinal ingredients and symptoms introduced in the main body of the text. In addition, he made use of actual empirical cases to enhance the persuasiveness of treatment methods and integrated medicine newly introduced from Song 宋 China (960–1279) into medicine familiarly used from before. Finally, he edited this text with a focus on important and simple yet efficacious treatment methods. The book continued to be used steadily following publication. It was additionally printed no fewer than twice by the government of the Chosŏn 朝鮮 dynasty (1392–1910), which ousted Koryŏ, with its clinical usefulness heightened through the supplementation of explanations on medicinal ingredients use in these processes. In particular, the quotation of sentences from Prescriptions for Emergency Use in medical texts published by the Chosŏn government implies that the utility of the medical knowledge in this work was amply acknowledged. The intended readership of the medical information in Prescriptions for Emergency Use was the not the general populace who lived in the Korean Peninsula in the thirteenth-fourteenth centuries. They not only lacked the financial means to pay physicians but also were illiterate, so that they could not even read medical texts. In order for this work to be effective, it was necessary for it to address those who could read medical texts and put their contents into practice. In the end, the author of this book assumed scholar-gentry equipped with academic knowledge as its readers and sought to provide medical information tailored to their level and to realize medical service through them. Through this work, it is possible to see in a very concrete and vivid manner how medical knowledge was disseminated and, furthermore, how medical knowledge thus disseminated was put to use in an era when medical resources were insufficient.


1991 ◽  
Vol 30 (04) ◽  
pp. 275-283 ◽  
Author(s):  
P. M. Pietrzyk

Abstract:Much information about patients is stored in free text. Hence, the computerized processing of medical language data has been a well-known goal of medical informatics resulting in different paradigms. In Gottingen, a Medical Text Analysis System for German (abbr. MediTAS) has been under development for some time, trying to combine and to extend these paradigms. This article concentrates on the automated syntax analysis of German medical utterances. The investigated text material consists of 8,790 distinct utterances extracted from the summary sections of about 18,400 cytopathological findings reports. The parsing is based upon a new approach called Left-Associative Grammar (LAG) developed by Hausser. By extending considerably the LAG approach, most of the grammatical constructions occurring in the text material could be covered.


2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Jung-wei Fan ◽  
Jianrong Li ◽  
Yves A. Lussier

Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics.


2021 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Xiaoying Gao ◽  
Yi Mei ◽  
S Ghosh ◽  
J Li

Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.


2013 ◽  
Vol 52 (05) ◽  
pp. 454-462 ◽  
Author(s):  
V. Stoicu-Tivadar ◽  
V. Topac

SummaryBackground: Patient empowerment is important in order to increase the quality of medical care and the life quality of the patients. An important obstacle for empowering patients is the language barrier the lay patient encounter when accessing medical information.Objectives: To design and develop a service that will help increase the understanding of medical language for lay persons.Methods: The service identifies and explains medical terminology from a given text by annotating the terms in the original text with the definition. It is based on an original terminology interpretation engine that uses a fuzzy matching dictionary. The service was implemented in two projects: a) into the server of a tele-care system (TELEASIS) with the purpose of adapting medical text assigned by medical personnel for the assisted patients. b) Into a dedicated web site that can adapt the medical language from raw text or from existing web pages.Result: The output of the service was evaluated by a group of persons, and the results indicate that such a system can increase the understanding of medical texts. Several design decisions were driven from the evaluation, and are being considered for future development. Other tests measuring accuracy and time performance for the fuzzy terminology recognition have been performed. Test results revealed good performance for accuracy and excellent results regarding time performance.Conclusion: The current version of the service increases the accessibility of medical language by explaining terminology with a good accuracy, while allowing the user to easily identify errors, in order to reduce the risk of incorrect terminology recognition.


2021 ◽  
Author(s):  
Youcheng Pan ◽  
Chenghao Wang ◽  
Baotian Hu ◽  
Yang Xiang ◽  
Xiaolong Wang ◽  
...  

BACKGROUND Electronic medical records (EMRs) are usually stored in relational databases that require structured query language (SQL) queries to retrieve information of interest. Effectively completing such queries is usually a challenging task for medical experts due to the barriers in expertise. However, existing text-to-SQL generation studies have not been fully embraced in the medical domain. OBJECTIVE The objective of this study was to propose a neural generation model, which can jointly consider the characteristics of medical text and the SQL structure, to automatically transform medical texts to SQL queries for EMRs. METHODS In contrast to regarding the SQL query as an ordinary word sequence, the syntax tree, introduced as an intermediate representation, is more in line with the tree-structure nature of SQL and also can effectively reduce the search space during generation. We proposed a medical text-to-SQL model (MedTS), which employed a pre-trained BERT as the encoder and leveraged a grammar-based LSTM as the decoder to predict the tree-structured intermediate representation that can be easily transformed to the final SQL query. Experiments are conducted on the MIMICSQL dataset and five competitor methods are compared. RESULTS Experimental results demonstrated that MedTS achieved the accuracy of 0.770 and 0.888 on the test set in terms of logic form and execution respectively, which significantly outperformed the existing state-of-the-art methods. Further analyses proved that the performance on each component of the generated SQL was relatively balanced and has substantial improvements. CONCLUSIONS The proposed MedTS was effective and robust for improving the performance of medical text-to-SQL generation, indicating strong potentials to be applied in the real medical scenario.


2019 ◽  
Vol 11 (12) ◽  
pp. 255 ◽  
Author(s):  
Li Qing ◽  
Weng Linhong ◽  
Ding Xuehai

Medical text categorization is a specific area of text categorization. Classification for medical texts is considered a special case of text classification. Medical text includes medical records and medical literature, both of which are important clinical information resources. However, medical text contains complex medical vocabularies, medical measures, which has problems with high-dimensionality and data sparsity, so text classification in the medical domain is more challenging than those in other general domains. In order to solve these problems, this paper proposes a unified neural network method. In the sentence representation, the convolutional layer extracts features from the sentence and a bidirectional gated recurrent unit (BIGRU) is used to access both the preceding and succeeding sentence features. An attention mechanism is employed to obtain the sentence representation with the important word weights. In the document representation, the method uses the BIGRU to encode the sentences, which is obtained in sentence representation and then decode it through the attention mechanism to get the document representation with important sentence weights. Finally, a category of medical text is obtained through a classifier. Experimental verifications are conducted on four medical text datasets, including two medical record datasets and two medical literature datasets. The results clearly show that our method is effective.


Author(s):  
Ali Akbar Zeinali

Medical language, as many technical languages, is rich with morphologically complex words. The increasing number of foreign words and specific terms incorporated into the native language are due to the ongoing development of technology and science. Many problems appear in medical translation when the Persian translators try to employ non-Persian or imported words in medical texts, in which multiple equivalents may be created for one particular word based on the individual preferences of authors and translators in the target language. According to this study, following the analysis of the data based on the applied translation procedures and word formation processes, the compatibility of the resulted characteristics has been investigated based on Sager's naming criteria and it is concluded that the main problem is due to the translation procedures of borrowing and substitution.


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