Predicting Social Anxiety Treatment Outcome based on Therapeutic Email Conversations

Hoogendoorn, Mark; Berger, Thomas; Schulz, Ava; Stolz, Timo; Szolovits, Peter (2017). Predicting Social Anxiety Treatment Outcome based on Therapeutic Email Conversations. IEEE journal of biomedical and health informatics, PP(99), pp. 1-11. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2016.2601123

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Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more treatments involve computer-based exercises or electronic conversations between patient and therapist. In this paper, we study predictive modeling using writings of patients under treatment for a social anxiety disorder. We extract a wealth of information from the text written by patients including their usage of words, the topics they talk about, the sentiment of the messages, and the style of writing. In addition, we study trends over time with respect to those measures. We then apply machine learning algorithms to generate the predictive models. Based on a dataset of 69 patients we are able to show that we can predict therapy outcome with an Area Under the Curve (AUC) of 0.83 halfway through the therapy and with a precision of 0.78 when using the full data (i.e., the entire treatment period). Due to the limited number of participants it is hard to generalize the results, but they do show great potential in this type of information.

Item Type:

Journal Article (Original Article)

Division/Institute:

07 Faculty of Human Sciences > Institute of Psychology > Clinical Psychology and Psychotherapy
07 Faculty of Human Sciences > Institute of Psychology

UniBE Contributor:

Berger, Thomas; Schulz, Ava and Stolz, Timo Johannes

Subjects:

600 Technology > 610 Medicine & health
100 Philosophy > 150 Psychology

ISSN:

2168-2194

Publisher:

Institute of Electrical and Electronics Engineers

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Thomas Berger

Date Deposited:

29 May 2017 12:06

Last Modified:

29 May 2017 12:06

Publisher DOI:

10.1109/JBHI.2016.2601123

PubMed ID:

27542187

BORIS DOI:

10.7892/boris.94891

URI:

https://boris.unibe.ch/id/eprint/94891

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