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Towards a topic modeling approach to semi-automatically detect self-reported stroke symptoms (FAST symptoms) and their correlation with aphasia types

Author

Listed:
  • Emmanouil S. Rigas

    (Aristotle University of Thessaloniki)

  • Tatiana Pourliaka

    (University of Macedonia)

  • Maria Papoutsoglou

    (University of Cyprus)

  • Hariklia Proios

    (University of Macedonia)

Abstract

This paper examines whether stroke survivors express that they suffered stroke symptoms on a question from the self-reported informal part of comprehensive test for communication ability. More specifically, whether they spontaneously refer to FAST (Face, Arm, Speech, Time) symptoms. Is there a connection between these FAST symptoms and the type of aphasia that they were diagnosed with? The present study involved 106 individuals with stroke; the majority having suffered an ischemic stroke. To carry out the research, statistical analysis, and analysis of the language through machine learning were performed on their answers to the one of the informal questions from the test “Why are you here today?”. All stories are in the Greek language. Replies were analyzed using term frequency and topic modelling techniques and have shown that terminology belonging to FAST symptoms appears in 37% of the reports. Further investigation of whether these FAST symptoms are associated with a particular type of aphasia showed that a statistically significant correlation between certain symptoms and diagnostic category of aphasia does not exist. However, further analysis with a larger dataset is needed.

Suggested Citation

  • Emmanouil S. Rigas & Tatiana Pourliaka & Maria Papoutsoglou & Hariklia Proios, 2023. "Towards a topic modeling approach to semi-automatically detect self-reported stroke symptoms (FAST symptoms) and their correlation with aphasia types," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1321-1336, April.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01417-6
    DOI: 10.1007/s11135-022-01417-6
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    References listed on IDEAS

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    1. Pietro Giorgio Lovaglio & Mario Mezzanzanica & Emilio Colombo, 2020. "Comparing time series characteristics of official and web job vacancy data," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 85-98, February.
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    5. Papoutsoglou, Maria & Rigas, Emmanouil S. & Kapitsaki, Georgia M. & Angelis, Lefteris & Wachs, Johannes, 2022. "Online labour market analytics for the green economy: The case of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
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