ArticleIEEE transactions on affective computing
Multi-label Multi-task Deep Learning for Behavioral Coding.
Article in IEEE transactions on affective computing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The trial behind it
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Who cites it
12 citing papers in PubMed.
- Characterizing Fidelity Monitoring Practices in Community Behavioral Health Care.The journal of behavioral health services & research · 2026Article
- Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology.Cognitive and behavioral practice · 2024Article
- Neural Conversational Agent for Weight Loss Counseling: Protocol for an Implementation and Feasibility Study.JMIR research protocols · 2024Article
- Feasibility of virtual reality and machine learning to assess personality traits in an organizational environment.Frontiers in psychology · 2024Article
- Logic-driven Indirect Supervision: An Application to Crisis Counseling.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023Article
- Diverse Task Classification from Activation Patterns of Functional Neuro-Images Using Feature Fusion Module.Sensors (Basel, Switzerland) · 2023Article
- Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.BMC health services research · 2022Article
- Knowledge and Attitudes Toward an Artificial Intelligence-Based Fidelity Measurement in Community Cognitive Behavioral Therapy Supervision.Administration and policy in mental health · 2022Article
- Automated evaluation of psychotherapy skills using speech and language technologies.Behavior research methods · 2022Article
- Collective intelligence in teams: Contextualizing collective intelligent behavior over time.Frontiers in psychology · 2022Review
- Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.PloS one · 2021Article
- Towards End-2-end Learning for Predicting Behavior Codes from Spoken Utterances in Psychotherapy Conversations.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We propose a methodology for estimating human behaviors in psychotherapy sessions using mutli-label and multi-task learning paradigms. We discuss the problem of behavioral coding in which data of human interactions is the annotated with labels to describe relevant human behaviors of interest. We describe two related, yet distinct, corpora consisting of therapist client interactions in psychotherapy sessions. We experimentally compare the proposed learning approaches for estimating behaviors of interest in these datasets. Specifically, we compare single and multiple label learning approaches, single and multiple task learning approaches, and evaluate the performance of these approaches when incorporating turn context. We demonstrate the prediction performance gains which can be achieved by using the proposed paradigms and discuss the insights these models provide into these complex interactions.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.