Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts (2020)

Abstract: Accurately diagnosing depression is difficult– requiring time-intensive interviews, assessments, and analysis. Hence, automated methods that can assess linguistic patterns in these interviews could help psychiatric professionals make faster, more informed decisions about diagnosis. We propose JLPC, a model that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. This latent categorization allows the model to define high-level conversational contexts that influence patterns of language in depressed individuals. We show that the proposed model not only outperforms competitive baselines, but that its latent prompt categories provide psycholinguistic insights about depression.

## introduction depression is a dangerous disease that effects many. a 2017 study by @xcite finds that one in five us adults experienced depression symptoms in their lifetime. weinberger et al. also identify depression as a significant risk factor for suicidal behavior. unfortunately, professional help for depression is not only stigmatized, but also expensive, timeconsuming and inaccessible to a large population. @xcite explain that there are no laboratory tests for diagnosing psychiatric disorders; instead these disorders must be identified through screening interviews of potential patients that require time-intensive analysis by medical experts. this has motivated developing automated depression detection systems that can provide confidential, inexpensive and timely preliminary triaging that can help individuals in seeking help from medical experts. such systems can help psychiatric professionals by analyzing interviewees for predictive behavioral indicators that could serve as additional evidence @xcite . language is a well-studied behavioral indicator for depression. psycholinguistic studies by @xcite , @xcite identify patterns of language in depressed individuals, such as focus on self and detachment from community. to capitalize on this source of information, recent work has proposed deep learning models that leverage linguistic features to identify depressed individuals @xcite . such deep learning models achieve high performance by uncovering complex, unobservable patterns in data at the cost of transparency. however, in the sensitive problem domain of diagnosing psychiatric disorders, a model should offer insight about its functionality in order for it to be useful as a clinical support tool. one way for a model to do this is utilizing the structure of the input (interview transcript) to identify patterns of conversational contexts that can help professionals in understanding how the model behaves in different contexts. a typical interview is structured as pairs of prompts and responses such that participant responses follow interviewer prompts (such as "how have you been feeling lately?"). intuitively, each interviewer prompt serves as a context that informs how its response should be analyzed. for example, a short response like "yeah" could communicate agreement in response to a question such as "are you happy you did that?", but the same response could signal taciturnity or withdrawal (indicators of depression) in response to an encouraging prompt like "nice!". to enable such contextdependent analysis, the model should be able to group prompts based on the types of conversa-tional context they provide. to accomplish this, we propose a neural joint latent prompt categorization (jlpc) model that infers latent prompt categories. depending on a prompt's category, the model has the flexibility to focus on different signals for depression in the corresponding response. this prompt categorization is learned jointly with the end task of depression prediction. beyond improving prediction accuracy, the latent prompt categorization makes the proposed model more transparent and offers insight for expert analysis. to demonstrate this, we analyze learned prompt categories based on existing psycholinguistic research. we also test existing hypotheses about depressed language with respect to these prompt categories. this not only offers a window into the model's working, but also can be used to design better clinical support tools that analyze linguistic cues in light of the interviewer prompt context. our key contributions are: ## joint latent prompt categorization we propose a joint latent prompt categorization (jlpc) model that jointly learns to predict depression from interview transcripts while grouping interview prompts into latent categories. 1 . the general problem of classifying interview text is defined as follows: let x denote the set of n interview transcripts. each interview x i is a sequence of j conversational turns consisting of interviewer's prompts and participant's responses: our approach assumes that prompts and responses are represented as embeddings p @xmath0. we hypothesize that prompts can be grouped into latent categories (k in number) such that corresponding responses will exhibit unique, useful patterns. to perform a soft assignment of prompts to categories, for each prompt, our model computes a category member- it represents the probability distribution for the j th prompt of the i th interview over each of k latent categories. h ij is computed as a function φ of p ij and trainable parameters θ ci (illustrated as the category inference layer in figure 1 ): based on these category memberships for each prompt, the model then analyzes the corresponding responses so that unique patterns can be learned for each category. specifically, we form k category-aware response aggregations. each of these aggregations, rk @xmath1, is a category-aware representation of all responses of the i th interview with respect to the k th category. we then compute the output class probability vector y i as a function ψ of the response aggregations [ r1 i , • • • , rk i ] and trainable parameters θ d (illustrated as the decision layer in figure 1 ). the predicted label ŷi is selected as the class with the highest probability based on y i . ## dataset we evaluate our model on the distress analysis interview corpus (daic) @xcite . daic consists of text transcripts of interviews designed to emulate a clinical assessment for depression. the interviews are conducted between human participants and a human-controlled digital avatar. each interview is labeled with a binary depression rating based on a score threshold for the 9th revision of the patient health questionnaire (phq-9). in total, there are 170 interviews, with 49 participants identified as depressed. to achieve stable and robust results given the small size of the daic dataset, we report performance over 10 separate splits of the dataset into training, validation, and test sets. for each split, 70% is used as training data, and 20% of the training data is set aside as validation data. ## experiments we now describe our experiments and analysis. ## related work much work exists at the intersection of natural language processing (nlp), psycholinguistics, and clinical psychology. for example, exploring correlations between counselor-patient interaction dynamics and counseling outcomes @xcite ; studying linguistic development of mental healthcare counsellors @xcite ; identifying differences in how people disclose mental illnesses across gender and culture @xcite ; predicting a variety of mental health conditions from social media posts @xcite @xcite ; and analyzing well-being @xcite and distress @xcite . specifically, many researchers have used nlp methods for identifying depression @xcite . they focus on for predicting depression from twitter posts @xcite @xcite , facebook updates @xcite , student essays @xcite , etc. previous works have also focused on predicting depression severity from screening interview data @xcite @xcite . unlike ours, these approaches rely on audio, visual, and text input. more recent approaches are based on deep learning. yang et al. (2017) other approaches are similar to ours in the sense that they utilize the structure provided by interview prompts. al @xcite and @xcite propose models that extract separate sets of features for responses to each unique prompt in their corpus. however, these approaches require manually identifying unique prompts. our model can instead automatically learn new, task-specific categorization of prompts. @xcite perform a k-means clustering of prompt to assign prompts to latent dialogue act categories. these are used as features in a neural dialogue system. our approach expands upon this idea of incorporating a separate unsupervised clustering step by allowing the learning goal to influence the clustering. our approach is also related to that of @xcite in that it automatically categorizes various parts of the conversation. however, they use domain-specific handcrafted features and discrete latent variables for this categorization. our approach instead can leverage the neural architecture to automatically identify features useful for this categorization. to the best of our knowledge, our approach is the first deep learning approach that jointly categorizes prompts to learn context-dependent patterns in responses. ## conclusion this paper addressed the problem of identifying depression from interview transcripts. the proposed model analyzes the participant's responses in light of various categories of prompts provided by the interviewer. the model jointly learns these prompt categories while identifying depression. we show that the model outperforms competitive baselines and we use the prompt categorization to investigate various psycholinguistic hypotheses. depression prediction is a difficult task which requires especially trained experts to conduct interviews and do their detailed analysis @xcite . while the absolute performance of our model is low for immediate practical deployment, it improves upon existing methods and at the same time, unlike modern methods, provides insight about the model's workflow. for example, our findings show how language of depressed individuals changes when interviewers use backchannels to encourage continued speech. we hope that this combination will encourage the research community to make more progress in this direction. future work can further investigate temporal patterns in how language used by depressed people evolves over the course of an interaction.