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2020 | OriginalPaper | Buchkapitel

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

verfasst von : Iuliia Lenivtceva, Evgenia Slasten, Mariya Kashina, Georgy Kopanitsa

Erschienen in: Computational Science – ICCS 2020

Verlag: Springer International Publishing

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Abstract

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.

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Titel: Applicability of Machine Learning Methods to Multi-label Medical Text Classification
verfasst von: Iuliia Lenivtceva
Evgenia Slasten
Mariya Kashina
Georgy Kopanitsa
Verlag: Springer International Publishing
Buch: Computational Science – ICCS 2020
Print ISBN: 978-3-030-50422-9

Electronic ISBN: 978-3-030-50423-6

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-50423-6_38

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