DOI : https://doi.org/10.18517/ijaseit.11.6.13602

Routine Outcome Monitoring in Psychotherapy Treatment Using Sentiment-Topic Modelling Approach

Noor Fazilla Abd Yusof (1), Chenghua Lin (2)
(1) Centre for Advanced Computing Technology(C-ACT), Fakulti Teknologi Maklumat dan Komunikasi(FTMK), Universiti Teknikal Malaysia Melaka, Malaysia
(2) Computer Science Department, University of Sheffield, United Kingdom,
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How to cite (IJASEIT) :
Abd Yusof, Noor Fazilla, and Chenghua Lin. “Routine Outcome Monitoring in Psychotherapy Treatment Using Sentiment-Topic Modelling Approach”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 6, Dec. 2021, pp. 2428-35, doi:10.18517/ijaseit.11.6.13602.
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Despite the importance of emphasizing the right psychotherapy treatment for an individual patient, assessing the outcome of the therapy session is equally crucial. Evidence showed that continuous monitoring of patient's progress could significantly improve the therapy outcomes to an expected change. The patient's progress can be tracked closely to help clinicians identify not progressing in the treatment by monitoring the outcome. This monitoring can help the clinician consider any necessary actions for the patient's treatment as early as possible, e.g., recommend different types of treatment or adjust the style of approach.  Currently, the evaluation system is based on the clinical-rated and self-report questionnaires that measure patients' progress pre-and post-treatment. While outcome monitoring tends to improve therapy outcomes, there are many challenges in the current method, e.g., time and financial burden for administering questionnaires, scoring, and analyzing the results. Therefore, a computational method for measuring and monitoring patient progress throughout treatment is needed to enhance the likelihood of positive treatment outcomes. This paper focuses on developing a computational method using a Dynamic Joint-Sentiment-Topic model (dJST) to measure and monitor the patient treatment outcome by tracking patient’s current and recurrent views of topic and sentiment. We identified the sentiment and topic trend evolved throughout treatments for each therapy session on the author's level. Our results show that this computational method could potentially lead to an inexpensive clinical monitoring tool to evaluate patients' progress during psychotherapy.

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