scholarly journals Experiencer Detection and Automated Extraction of a Family Disease Tree from Medical Texts in Russian Language

2020 ◽  
pp. 603-612 ◽  
Author(s):  
Ksenia Balabaeva ◽  
Sergey Kovalchuk
Keyword(s):  
Russian Language ◽  
Automated Extraction ◽  
Medical Texts

scholarly journals Comparison of Word Embeddings for Extraction from Medical Records

2019 ◽  
Vol 16 (22) ◽  
pp. 4360
Author(s):  
Aleksei Dudchenko ◽  
Georgy Kopanitsa
Keyword(s):  
Neural Network ◽  
Medical Records ◽  
Data Extraction ◽  
Russian Language ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Network Architectures ◽  
Medical Texts ◽  
Convolutional Network ◽  
Personalized Health

This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from texts available for decision support systems, supervised machine learning algorithms might be successfully applied. In this work, we developed and compared a prototype of a medical data extraction system based on different artificial neural network architectures to process free medical texts in the Russian language. Three classifiers were applied to extract entities from snippets of text. Multi-layer perceptron (MLP) and convolutional neural network (CNN) classifiers showed similar results to all three embedding models. MLP exceeded convolutional network on pipelines that used the embedding model trained on medical records with preliminary lemmatization. Nevertheless, the highest F-score was achieved by CNN. CNN slightly exceeded MLP when the biggest word2vec model was applied (F-score 0.9763).

scholarly journals Extraction of the Relations between Significant Pharmacological Entities in Russian-Language Internet Reviews on Medications

2021 ◽  
Author(s):  
Alexander Sboev ◽  
Anton Selivanov ◽  
Ivan Moloshnikov ◽  
Roman Rybka ◽  
Artem Gryaznov ◽  
...  
Keyword(s):  
Cross Validation ◽  
State Of The Art ◽  
Language Model ◽  
Russian Language ◽  
Medical Texts ◽  
Meaningful Information ◽  
Multiple Contexts ◽  
Single Text ◽  
Great Relevance ◽  
Drug Review

Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.

High throughput semi-automated extraction method for the creation of a collection from microbial fermentations of fungi and Actinomycetes

Planta Medica ◽  
2008 ◽  
Vol 74 (09) ◽  
Author(s):  
JR Tormo ◽  
N Tabanera ◽  
D Conway ◽  
P Ramos ◽  
A Redondo ◽  
...  
Keyword(s):  
High Throughput ◽  
Extraction Method ◽  
Automated Extraction ◽  
The Creation

TEACHING INFORMATICS IN A PEDAGOGICAL UNIVERSITY TAKING INTO ACCOUNT FOREIGN LANGUAGE STUDENTS

2019 ◽  
pp. 13-21
Author(s):  
M. S. Zubrilina ◽  
A. A. Zubrilin
Keyword(s):  
Foreign Students ◽  
Russian Language ◽  
Higher Education System ◽  
Russian Higher Education ◽  
State Pedagogical ◽  
State Pedagogical Institute ◽  
And Mathematics ◽  
Independent Work ◽  
Russian Universities ◽  
Theoretical Foundations

The emergence of an increasing number of foreign students in Russian universities indicates the importance of the Russian higher education system in the world community. At the same time, a new problem emerged on the agenda — how to train foreigners from abroad with high quality, taking into account their different readiness in mastering the Russian language and different subject training. The article describes the problems that foreign students face when studying informatics at a pedagogical university. The combined profiles, including “Informatics”, of the Faculty of Physics and Mathematics of Mordovian State Pedagogical Institute named after M. E. Evsevjev were the experimental research site. On the example of the “Theoretical Foundations of Informatics” discipline, the ways of teaching informatics are shown. Examples of assignments, including tasks for independent work, for teaching foreign students to informatics at the specified university are given.

A BRIEF ANALYSIS OF TIME-PREPOSITIONS IN THE RUSSIAN LANGUAGE

2019 ◽  
Vol 18 (28) ◽  
pp. 187-195
Author(s):  
Yabing Zhang
Keyword(s):  
Foreign Language ◽  
Foreign Students ◽  
Russian Language ◽  
Parts Of Speech ◽  
Organic Structure ◽  
Part Of Speech ◽  
True Meaning ◽  
Oral Speech ◽  
The Russian Language

This article is devoted to the problem of using Russian time-prepositions by foreigners, especially by the Chinese. An analysis of modern literature allows the author to identify the main areas of the work aimed at foreign students’ development of the skills and abilities to correctly build the prepositional combinations and continuously improve the communication skills by means of the Russian language. In this paper, the time-prepositions in the Russian language have been analyzed in detail; some examples of polysemantic use of prepositions, their semantic and stylistic shades alongside with possible errors made by foreign students are presented. The results of the study are to help in developing a system of teaching Russian time-prepositions to a foreign language audience, taking into account their native language, on the basis of the systemic and functional, communicative and activity-centred basis. The role of Russian time-prepositions in constructing word combinations has been identified; the need for foreign students’ close attention to this secondary part of speech has been specified. It has been stated that prepositions are the most dynamic and open type of secondary language units within the quantitative and qualitative composition of which regular changes take place. The research substantiates the need that students should be aware of the function of time-preposition in speech; they are to get acquainted with the main time-prepositions and their meanings, to distinguish prepositions and other homonymous parts of speech as well as to learn stylistic shades of time-prepositions. Some recommendations related to the means of mastering time-prepositions have been given: to target speakers to assimilate modern literary norms and, therefore, to teach them how to choose and use them correctly by means of linguistic keys that are intended to fill the word with true meaning, to give it an organic structure, an inherent form and an easy combinability in the texts and oral speech.

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