Behavioral Coding of Therapist Language in Addiction Counseling Using Recurrent Neural Networks

Xiao, Bo; Can, Doğan; Gibson, James; Imel, Zac E.; Atkins, David C.; Georgiou, Panayiotis G.; Narayanan, Shrikanth

doi:10.21437/interspeech.2016-1560

Cited by 34 publications

(48 citation statements)

References 13 publications

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“…The majority baseline illustrates the severity of the label imbalance problem. Xiao et al (2016), BiGRU generic , Can et al (2015) and Tanana et al (2016) We found that predicting using MLP(H n ) + MLP(v n ) performs better than just MLP(H n ).…”

Section: Resultsmentioning

confidence: 65%

“…Due to data scarcity and label confusion, various strategies are proposed to merge the labels into a coarser set. We adopt the grouping proposed byXiao et al (2016); the appendix gives more details.…”

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confidence: 99%

“…Other related work in no context exists (e.g.,Pérez-Rosas et al, 2017; Gibson et al, 2017), but they either do not outperform(Xiao et al, 2016) or use different data.…”

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confidence: 99%

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Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes

Cao

Tanana

Imel

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist. For both tasks, we define neural network models that build upon recent successes in dialogue modeling. Our experiments demonstrate that our models can outperform several baselines for both tasks. We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue.

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Section: Resultsmentioning

confidence: 65%

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confidence: 99%

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Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes

Cao

Tanana

Imel

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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“…• (Xiao et al, 2016) (denoted as Xiao2016): Their approach applies Bi-directional RNN to encode each utterance by both the utterance itself and its preceding one. There are two major differences between their method and ours: first, they did not consider temporality in their model, second, they did not use the previous MISC sequences as inputs.…”

Section: Methodsmentioning

confidence: 99%

“…However, reliable MISC coding is labor-intensive and requires domain expertise. Recent computational annotation methods have been proposed to automatically classify patients' behaviors within MI (Xiao et al, 2016;Pérez-Rosas et al, 2017;Gibson et al, 2017). To this end, Recurrent Neural Networks (RNN) that capture sequential information are applied for the classification of patient behavior.…”

Section: Introductionmentioning

confidence: 99%

Modeling Temporality of Human Intentions by Domain Adaptation

Huang

Liu

Carey

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Categorizing a patient's intentions during clinical interactions in general and within motivational interviewing specifically may improve decision making in clinical treatments. Within this paper, we propose a method that models the temporal flow of a conversation and the transition between topics by using domain adaptation on a clinical dialogue corpus comprising Motivational Interviewing (MI) sessions. We deploy Bi-LSTM and topic models jointly to learn theme shifts across different time stages within these hour-long MI sessions to assess the patient's intent to change their habits or to sustain them respectively. Our experiments show promising results and improvements after considering temporality in the classification task over our baseline. This result confirms and extends related literature that has manually identified that certain phases within MI sessions are more predictive of patient outcomes than others.

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Customized Training of Pretrained Language Models to Detect Post Intents in Online Health Support Groups

Giyahchi

Singh

Pechmann

2022

Multimodal AI in Healthcare

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Online support groups offer low-cost and accessible health and mental health support, but low engagement challenges their effectiveness. To encourage engagement and provide evidence-based responses appropriate to participants' needs, we propose an intent detection model for online support groups based on state-of-the-art natural language processing methods. Such a model enables a chatbot that can increase interactions and improve discussion quality. Posts in social media are often short and noisy, especially in group chat. Furthermore, many intents lack data, overlap and/or have specific priorities. We create a human-annotated dataset of posts with intent labels from 45 three-month online support groups for quitting smoking. We then train and examine models to predict the intent behind each post. To reduce the effect of noisy and sparse data, we fine-tune a massive pretrained language model. Also, to represent the unique relationships between intents, we design customized loss functions for training. Empirical evaluations show significant performance improvements with the proposed method; our best model obtains 95.5% accuracy. We also use a fine-grained set of intents and obtain higher accuracy compared to prior models on online health forums and communities. Accurate detection of fine-grained intents opens up new opportunities to improve online self-help support groups.

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Behavioral Coding of Therapist Language in Addiction Counseling Using Recurrent Neural Networks

Cited by 34 publications

References 13 publications

Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes

Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes

Modeling Temporality of Human Intentions by Domain Adaptation

Customized Training of Pretrained Language Models to Detect Post Intents in Online Health Support Groups

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