Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations

Abstract

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.

Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data

“…In addition, we want to use different sensor and usage data from the mobile phone including activity data and log data from follow ups to improve predictive performance. Also, following Hoogendoorn et al (2016) we will try to exploit free text in the predictions as well.…”

Introduction to the Special Section: Convergence of Automation Technology, Biomedical Engineering, and Health Informatics Toward the Healthcare 4.0

“…In addition, AI-based tools are already appearing in health or medical oriented applications that can be employed on wearable and networked smart devices [15]. This enables machines to sense, comprehend, learn, and act so they can perform administrative and clinical functions [16], [17]. Combining with life science, AI has the potential power to reshape the future of public health, community health, and healthcare delivery aiming to achieve a higher quality of life [18], [19].…”

Predictive modeling in e-mental health: A common language framework

“… 3 Perlis (2013) Socio-demographics, self-reported clinical data Treatment resistance Naïve Bayes, logistic regression, support vector machine, random forest Treatment resistance is predicted based on self-reported data. 3 Hoogendoorn et al (2017) Socio-demographics, emails sent by patient Treatment success Logistic regression, decision tree, random forest Treatment success is predicted based on the text contained in the emails sent by the patient to the therapist. 4 Kessing (1999) ICD-10 Depression rating Relapse risk, suicide risk Cox-regression The risk of relapse is significantly related to the severity of baseline and post-treatment depression.…”

“…An approach that utilizes free text written by patients is proposed in Hoogendoorn et al (2017) . They extracted features from the text messages sent by patients suffering from an anxiety disorder to their therapist as part of an anxiety treatment.…”