Abstract
In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings. We also incorporate temporal modeling to put this additional interpretability to action by parsing out topic similarities as a time series in a turn-level resolution. We believe this topic modeling framework can offer interpretable insights for the therapist to optimally decide his or her strategy and improve psychotherapy effectiveness.
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Lin, B., Bouneffouf, D., Cecchi, G., Tejwani, R. (2023). Neural Topic Modeling of Psychotherapy Sessions. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) Artificial Intelligence for Personalized Medicine. W3PHAI 2023. Studies in Computational Intelligence, vol 1106. Springer, Cham. https://doi.org/10.1007/978-3-031-36938-4_16
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