Russian Q&amp;A Method Study: From Naive Bayes to Convolutional Neural Networks

Nikolaev, K.; Malafeev, Alexey

doi:10.1007/978-3-030-11027-7_12

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…17 I. Nikishina 18 represented RusNLP search engine for academic papers with 0.64 average precision. Automatic question classification task for Russian free texts was solved by Nikolaev 19 on the manually annotated corpus. The best obtained accuracy score is 72.38% with CNN.…”

Section: Related Research Specificity Of Medical Recordsmentioning

confidence: 99%

The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource

Lenivtceva

Kopanitsa

2021

Methods Inf Med

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Background The larger part of essential medical knowledge is stored as free text which is complicated to process. Standardization of medical narratives is an important task for data exchange, integration, and semantic interoperability. Objectives The article aims to develop the end-to-end pipeline for structuring Russian free-text allergy anamnesis using international standards. Methods The pipeline for free-text data standardization is based on FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) to ensure semantic interoperability. The pipeline solves common tasks such as data preprocessing, classification, categorization, entities extraction, and semantic codes assignment. Machine learning methods, rule-based, and dictionary-based approaches were used to compose the pipeline. The pipeline was evaluated on 166 randomly chosen medical records. Results AllergyIntolerance resource was used to represent allergy anamnesis. The module for data preprocessing included the dictionary with over 90,000 words, including specific medication terms, and more than 20 regular expressions for errors correction, classification, and categorization modules resulted in four dictionaries with allergy terms (total 2,675 terms), which were mapped to SNOMED CT concepts. F-scores for different steps are: 0.945 for filtering, 0.90 to 0.96 for allergy categorization, 0.90 and 0.93 for allergens reactions extraction, respectively. The allergy terminology coverage is more than 95%. Conclusion The proposed pipeline is a step to ensure semantic interoperability of Russian free-text medical records and could be effective in standardization systems for further data exchange and integration.

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Section: Related Research Specificity Of Medical Recordsmentioning

confidence: 99%