Automatic allergy classification based on Russian unstructured medical texts
Most of the medical data in hospital information systems databases are stored in an unstructured form. Techniques for processing unstructured records are widely presented in scientific papers focused on English data. This paper proposes a method for intellectual analysis of unstructured allergy anam...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
2021-06-01
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| Series: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| Subjects: | |
| Online Access: | https://ntv.ifmo.ru/file/article/20517.pdf |
| _version_ | 1818442574800617472 |
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| author | Iuliia D. Lenivtceva Georgy D. Kopanitsa |
| author_facet | Iuliia D. Lenivtceva Georgy D. Kopanitsa |
| author_sort | Iuliia D. Lenivtceva |
| collection | DOAJ |
| description | Most of the medical data in hospital information systems databases are stored in an unstructured form. Techniques for processing unstructured records are widely presented in scientific papers focused on English data. This paper proposes a method for intellectual analysis of unstructured allergy anamnesis in Russian in order to identify the presence and type of allergy and intolerance of a patient. The method is based on machine learning algorithms and uses international standards for the exchange of medical data and terminology standards, such as FHIR and SNOMED CT. As a result of the experiment, about 12 thousand medical records were processed. F-measure for the developed classification models ranged from 0.93 to 0.96. The models showed high values of metrics for evaluating the effectiveness of the models. In the future, structured data can be used in models for predicting medical risks. Further development of methods for structuring medical texts will ensure the interoperability of medical data. |
| first_indexed | 2024-12-14T18:46:19Z |
| format | Article |
| id | doaj.art-d8fc923971f947ea9cfc7b5401fab1ac |
| institution | Directory Open Access Journal |
| issn | 2226-1494 2500-0373 |
| language | English |
| last_indexed | 2024-12-14T18:46:19Z |
| publishDate | 2021-06-01 |
| publisher | Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) |
| record_format | Article |
| series | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| spelling | doaj.art-d8fc923971f947ea9cfc7b5401fab1ac2022-12-21T22:51:22ZengSaint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki2226-14942500-03732021-06-0121343343610.17586/2226-1494-2021-21-3-433-436Automatic allergy classification based on Russian unstructured medical textsIuliia D. Lenivtceva0https://orcid.org/0000-0002-5572-5151Georgy D. Kopanitsa1https://orcid.org/0000-0002-6231-8036Engineer, ITMO University, Saint Petersburg, 197101, Russian FederationPhD, Leading Researcher, ITMO University, Saint Petersburg, 197101, Russian FederationMost of the medical data in hospital information systems databases are stored in an unstructured form. Techniques for processing unstructured records are widely presented in scientific papers focused on English data. This paper proposes a method for intellectual analysis of unstructured allergy anamnesis in Russian in order to identify the presence and type of allergy and intolerance of a patient. The method is based on machine learning algorithms and uses international standards for the exchange of medical data and terminology standards, such as FHIR and SNOMED CT. As a result of the experiment, about 12 thousand medical records were processed. F-measure for the developed classification models ranged from 0.93 to 0.96. The models showed high values of metrics for evaluating the effectiveness of the models. In the future, structured data can be used in models for predicting medical risks. Further development of methods for structuring medical texts will ensure the interoperability of medical data.https://ntv.ifmo.ru/file/article/20517.pdfmedical data structuringallergyintolerancemachine learningunstructured text analysisinteroperability |
| spellingShingle | Iuliia D. Lenivtceva Georgy D. Kopanitsa Automatic allergy classification based on Russian unstructured medical texts Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki medical data structuring allergy intolerance machine learning unstructured text analysis interoperability |
| title | Automatic allergy classification based on Russian unstructured medical texts |
| title_full | Automatic allergy classification based on Russian unstructured medical texts |
| title_fullStr | Automatic allergy classification based on Russian unstructured medical texts |
| title_full_unstemmed | Automatic allergy classification based on Russian unstructured medical texts |
| title_short | Automatic allergy classification based on Russian unstructured medical texts |
| title_sort | automatic allergy classification based on russian unstructured medical texts |
| topic | medical data structuring allergy intolerance machine learning unstructured text analysis interoperability |
| url | https://ntv.ifmo.ru/file/article/20517.pdf |
| work_keys_str_mv | AT iuliiadlenivtceva automaticallergyclassificationbasedonrussianunstructuredmedicaltexts AT georgydkopanitsa automaticallergyclassificationbasedonrussianunstructuredmedicaltexts |