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

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@article{IJASEIT13602,
   author = {Noor Fazilla Abd Yusof and Chenghua Lin},
   title = {Routine Outcome Monitoring in Psychotherapy Treatment Using Sentiment-Topic Modelling Approach},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {6},
   year = {2021},
   pages = {2428--2435},
   keywords = {Psychotherapy treatment; sentiment-topic model; progress monitoring outcome; depression.},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13602},
   doi = {10.18517/ijaseit.11.6.13602}
}

EndNote

%A Abd Yusof, Noor Fazilla
%A Lin, Chenghua
%D 2021
%T Routine Outcome Monitoring in Psychotherapy Treatment Using Sentiment-Topic Modelling Approach
%B 2021
%9 Psychotherapy treatment; sentiment-topic model; progress monitoring outcome; depression.
%! Routine Outcome Monitoring in Psychotherapy Treatment Using Sentiment-Topic Modelling Approach
%K Psychotherapy treatment; sentiment-topic model; progress monitoring outcome; depression.
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13602
%R doi:10.18517/ijaseit.11.6.13602
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 6
%@ 2088-5334

IEEE

Noor Fazilla Abd Yusof 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, pp. 2428-2435, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.6.13602.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Abd Yusof, Noor Fazilla
AU  - Lin, Chenghua
PY  - 2021
TI  - Routine Outcome Monitoring in Psychotherapy Treatment Using Sentiment-Topic Modelling Approach
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 6
Y2  - 2021
SP  - 2428
EP  - 2435
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Psychotherapy treatment; sentiment-topic model; progress monitoring outcome; depression.
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13602
DO  - 10.18517/ijaseit.11.6.13602

RefWorks

RT Journal Article
ID 13602
A1 Abd Yusof, Noor Fazilla
A1 Lin, Chenghua
T1 Routine Outcome Monitoring in Psychotherapy Treatment Using Sentiment-Topic Modelling Approach
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 6
YR 2021
SP 2428
OP 2435
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Psychotherapy treatment; sentiment-topic model; progress monitoring outcome; depression.
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13602
DO  - 10.18517/ijaseit.11.6.13602