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Topic Identification from Spanish Unstructured Health Texts

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Applied Technologies (ICAT 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1388))

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Abstract

Topic Models allow to extract topics from documents and classify them. In this work, Latent Dirichlet Allocation model was applied to extract topics from documents with medical information. 220 digital documents written in Spanish were used, these documents have information about different health conditions. A pre-processing was carried out, which implies tokenization, stop words elimination and lemmatization, to define the medical data or terms that will represent the documents. Subsequently, a document representation was made through a document-term matrix. An important step was to use a medical glossary based on terminology extracted from Internet to assign weights to the terms. LDA was applied and two new matrices were obtained: a document-topic matrix and a topic-term matrix. 25 topics were identified, they can be visualized by heat maps, word cloud and an interactive tool called PyLDAvis. The application was developed in Phyton using some libraries such as Spacy, Scikit-learn, Tmtoolkit, PyLDAvis among others.

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Notes

  1. 1.

    https://temu.bsc.es/BARR2/.

  2. 2.

    https://tmtoolkit.readthedocs.io/en/latest/index.html.

  3. 3.

    https://pyldavis.readthedocs.io/en/latest/.

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Author information

Authors and Affiliations

  1. Universidad Técnica Particular de Loja, San Cayetano Alto, 1101608, Loja, Ecuador

    Andrea Mena & Ruth Reátegui

Authors
  1. Andrea Mena
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  2. Ruth Reátegui
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Corresponding author

Correspondence to Ruth Reátegui .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Miguel Botto-Tobar

  2. Universidad de las Fuerzas Armadas (ESPE), Quito, Ecuador

    Sergio Montes León

  3. Escuela Politécnica Nacional, Quito, Ecuador

    Oscar Camacho

  4. Escuela Politécnica Nacional, Quito, Ecuador

    Danilo Chávez

  5. Universidad Técnica Particular de Loja, Loja, Ecuador

    Pablo Torres-Carrión

  6. Universidad Técnica del Norte, Ibarra, Ecuador

    Marcelo Zambrano Vizuete

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Mena, A., Reátegui, R. (2021). Topic Identification from Spanish Unstructured Health Texts. In: Botto-Tobar, M., Montes León, S., Camacho, O., Chávez, D., Torres-Carrión, P., Zambrano Vizuete, M. (eds) Applied Technologies. ICAT 2020. Communications in Computer and Information Science, vol 1388. Springer, Cham. https://doi.org/10.1007/978-3-030-71503-8_27

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  • DOI: https://doi.org/10.1007/978-3-030-71503-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71502-1

  • Online ISBN: 978-3-030-71503-8

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