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2020 | OriginalPaper | Chapter

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

Authors : Ksenia Balabaeva, Sergey Kovalchuk

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

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Abstract

Text descriptions in natural language are an essential part of electronic health records (EHRs). Such descriptions usually contain facts about patient’s life, events, diseases and other relevant information. Sometimes it may also include facts about their family members. In order to find the facts about the right person (experiencer) and convert the unstructured medical text into structured information, we developed a module of experiencer detection. We compared different vector representations and machine learning models to get the highest quality of 0.96 f-score for binary classification and 0.93 f-score for multi-classification. Additionally, we present the results plotting the family disease tree.

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Title: Experiencer Detection and Automated Extraction of a Family Disease Tree from Medical Texts in Russian Language
Authors: Ksenia Balabaeva
Sergey Kovalchuk
Publisher: Springer International Publishing
Book: Computational Science – ICCS 2020
Print ISBN: 978-3-030-50422-9

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

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-50423-6_45

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