GDPR Compliant Collection of Therapist-Patient-Dialogues

According to the Global Burden of Disease list provided by the World Health Organization (WHO), mental disorders are among the most debilitating disorders.To improve the diagnosis and the therapy effectiveness in recent years, researchers have tried to identify individual biomarkers. Gathering neurobiological data however, is costly and time-consuming. Another potential source of information, which is already part of the clinical routine, are therapist-patient dialogues. While there are some pioneering works investigating the role of language as predictors for various therapeutic parameters, for example patient-therapist alliance, there are no large-scale studies. A major obstacle to conduct these studies is the availability of sizeable datasets, which are needed to train machine learning models. While these conversations are part of the daily routine of clinicians, gathering them is usually hindered by various ethical (purpose of data usage), legal (data privacy) and technical (data formatting) limitations. Some of these limitations are particular to the domain of therapy dialogues, like the increased difficulty in anonymisation, or the transcription of the recordings. In this paper, we elaborate on the challenges we faced in starting our collection of therapist-patient dialogues in a psychiatry clinic under the General Data Privacy Regulation of the European Union with the goal to use the data for Natural Language Processing (NLP) research. We give an overview of each step in our procedure and point out the potential pitfalls to motivate further research in this field.

NLP meets psychotherapy: Using predicted client emotions and self-reported client emotions to measure emotional coherence

Emotions are experienced and expressed through various response systems. Coherence between emotional experience and emotional expression is considered important to clients' well being. To date, emotional coherence (EC) has been studied at a single time point using lab-based tasks with relatively small datasets. No study has examined EC between the subjective experience of emotions and emotion expression in therapy or whether this coherence is associated with clients' well being. Natural language Processing (NLP) approaches have been applied to identify emotions from psychotherapy dialogue, which can be implemented to study emotional processes on a larger scale. However, these methods have yet to be used to study coherence between emotional experience and emotional expression over the course of therapy and whether it relates to clients' well-being. This work presents an end-to-end approach where we use emotion predictions from our transformer based emotion recognition model to study emotional coherence and its diagnostic potential in psychotherapy research. We first employ our transformer based approach on a Hebrew psychotherapy dataset to automatically label clients' emotions at utterance level in psychotherapy dialogues. We subsequently investigate the emotional coherence between clients' self-reported emotional states and our model-based emotion predictions. We also examine the association between emotional coherence and clients' well being. Our findings indicate a significant correlation between clients' self-reported emotions and positive and negative emotions expressed verbally during psychotherapy sessions. Coherence in positive emotions was also highly correlated with clients well-being. These results illustrate how NLP can be applied to identify important emotional processes in psychotherapy to improve diagnosis and treatment for clients suffering from mental-health problems.

Semantic Similarity Models for Depression Severity Estimation

Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting users' symptom severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving 30\% improvement over state of the art in terms of measuring depression severity.