Applicability of Machine Learning Methods to Multi-label Medical Text Classification

Structuring medical text using international standards allows to improve interoperability and quality of predictive modelling. Medical text classification task facilitates information extraction. In this work we investigate the applicability of several machine learning models and classifier chains (...

Descripción completa

Detalles Bibliográficos
Autores principales: Lenivtceva, Iuliia, Slasten, Evgenia, Kashina, Mariya, Kopanitsa, Georgy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303696/
http://dx.doi.org/10.1007/978-3-030-50423-6_38
_version_ 1783548115491487744
author Lenivtceva, Iuliia
Slasten, Evgenia
Kashina, Mariya
Kopanitsa, Georgy
author_facet Lenivtceva, Iuliia
Slasten, Evgenia
Kashina, Mariya
Kopanitsa, Georgy
author_sort Lenivtceva, Iuliia
collection PubMed
description Structuring medical text using international standards allows to improve interoperability and quality of predictive modelling. Medical text classification task facilitates information extraction. In this work we investigate the applicability of several machine learning models and classifier chains (CC) to medical unstructured text classification. The experimental study was performed on a corpus of 11671 manually labeled Russian medical notes. The results showed that using CC strategy allows to improve classification performance. Ensemble of classifier chains based on linear SVC showed the best result: 0.924 micro F-measure, 0.872 micro precision and 0.927 micro recall.
format Online
Article
Text
id pubmed-7303696
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-73036962020-06-19 Applicability of Machine Learning Methods to Multi-label Medical Text Classification Lenivtceva, Iuliia Slasten, Evgenia Kashina, Mariya Kopanitsa, Georgy Computational Science – ICCS 2020 Article Structuring medical text using international standards allows to improve interoperability and quality of predictive modelling. Medical text classification task facilitates information extraction. In this work we investigate the applicability of several machine learning models and classifier chains (CC) to medical unstructured text classification. The experimental study was performed on a corpus of 11671 manually labeled Russian medical notes. The results showed that using CC strategy allows to improve classification performance. Ensemble of classifier chains based on linear SVC showed the best result: 0.924 micro F-measure, 0.872 micro precision and 0.927 micro recall. 2020-05-23 /pmc/articles/PMC7303696/ http://dx.doi.org/10.1007/978-3-030-50423-6_38 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Lenivtceva, Iuliia
Slasten, Evgenia
Kashina, Mariya
Kopanitsa, Georgy
Applicability of Machine Learning Methods to Multi-label Medical Text Classification
title Applicability of Machine Learning Methods to Multi-label Medical Text Classification
title_full Applicability of Machine Learning Methods to Multi-label Medical Text Classification
title_fullStr Applicability of Machine Learning Methods to Multi-label Medical Text Classification
title_full_unstemmed Applicability of Machine Learning Methods to Multi-label Medical Text Classification
title_short Applicability of Machine Learning Methods to Multi-label Medical Text Classification
title_sort applicability of machine learning methods to multi-label medical text classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303696/
http://dx.doi.org/10.1007/978-3-030-50423-6_38
work_keys_str_mv AT lenivtcevaiuliia applicabilityofmachinelearningmethodstomultilabelmedicaltextclassification
AT slastenevgenia applicabilityofmachinelearningmethodstomultilabelmedicaltextclassification
AT kashinamariya applicabilityofmachinelearningmethodstomultilabelmedicaltextclassification
AT kopanitsageorgy applicabilityofmachinelearningmethodstomultilabelmedicaltextclassification

Ejemplares similares