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Analysis of Topic Propagation in Therapy Sessions Using Partially Labeled Latent Dirichlet Allocation

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Artificial Intelligence in Health (AIH 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11326))

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Abstract

The full comprehension of how topics change within psychotherapeutic conversation is key for assessment and therapeutic strategies to adopt by the counselor to the patients. That might enable artificial intelligence (AI) approaches to recommend the most suitable strategy for a new patient. Basically, understanding the topics dynamics of previous cases allows choosing the best therapy to perform for new patients depending on their current conversations.

In this paper we leverage Partially Labeled Dirichlet Allocation with the goal to detect and track topics in real-life psychotherapeutic conversations. On the one hand, the detection of topics allows us identifying the semantic themes of the current therapeutic conversation and predicting topics ad-hoc for each talk-turn between the patient and the counselor. On the other hand, the tracking of topics is key to understand and explore the dynamics of the conversation giving insights and tips on logic and strategy to adopt.

We point out that the entire conversation is structured and modeled according to a sequence of ongoing topics that might propagate through each talk-turn. We present a new method that combines topic modeling and transitions matrices that gives important information to counselors for their therapeutic strategies.

Authors are listed in alphabetic order since their contributions have been equally distributed.

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Notes

  1. 1.

    Image taken from Wikipedia https://en.wikipedia.org/wiki/Cognitive_behavioral_therapy.

  2. 2.

    http://www.discursis.com/.

  3. 3.

    http://ckip.iis.sinica.edu.tw/taxonomy.

  4. 4.

    http://www.apa.org.

  5. 5.

    For example, the word Family could correspond to a top level topic, while Family violence and Child abuse would be associated to the second and third levels respectively. Up to 575 subjects have been used in the three levels in total.

  6. 6.

    https://dsm.psychiatryonline.org.

  7. 7.

    https://en.wikipedia.org/wiki/Category:Main_topic_classifications.

  8. 8.

    http://www.nltk.org/.

  9. 9.

    https://nlp.stanford.edu/software/tmt/tmt-0.4/.

  10. 10.

    http://aksw.org/Projects/Palmetto.html.

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Authors and Affiliations

  1. Mathematics and Computer Science Department, University of Cagliari, Via Ospedale 72, 09124, Cagliari, Italy

    Ilyas Chaoua & Diego Reforgiato Recupero

  2. Philips Research, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands

    Sergio Consoli, Aki Härmä & Rim Helaoui

Authors
  1. Ilyas Chaoua
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  2. Sergio Consoli
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  3. Aki Härmä
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  4. Rim Helaoui
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  5. Diego Reforgiato Recupero
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Corresponding author

Correspondence to Sergio Consoli .

Editor information

Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Fernando Koch

  2. IIIA-CSIC and Autonomous University of Barcelona, Barcelona, Spain

    Andrew Koster

  3. Universitat Rovira i Virgili, Tarragona, Spain

    David Riaño

  4. Università di Bologna, Cesena, Italy

    Sara Montagna

  5. HES-SO Valais Wallis, Sierre, Switzerland

    Michael Schumacher

  6. VU University Amsterdam, Amsterdam, The Netherlands

    Annette ten Teije

  7. Karolinska Institute, Stockholm, Sweden

    Christian Guttmann

  8. University of Ulm, Ulm, Germany

    Manfred Reichert

  9. State University of New York, Oswego, NY, USA

    Isabelle Bichindaritz

  10. Imperial College London, London, UK

    Pau Herrero

  11. University of Erlangen and Nuremberg, Erlangen, Germany

    Richard Lenz

  12. University of Girona, Girona, Spain

    Beatriz López

  13. Ohio University, Athens, OH, USA

    Cindy Marling

  14. Oxford Brookes University, Oxford, UK

    Clare Martin

  15. Università del Piemonte Orientale, Alessandria, Italy

    Stefania Montani

  16. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, UK

    Nirmalie Wiratunga

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Chaoua, I., Consoli, S., Härmä, A., Helaoui, R., Reforgiato Recupero, D. (2019). Analysis of Topic Propagation in Therapy Sessions Using Partially Labeled Latent Dirichlet Allocation. In: Koch, F., et al. Artificial Intelligence in Health. AIH 2018. Lecture Notes in Computer Science(), vol 11326. Springer, Cham. https://doi.org/10.1007/978-3-030-12738-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-12738-1_5

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