Evidence map›Paper›PMID 36704712›Full record

ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2019

IMPROVING THE PREDICTION OF THERAPIST BEHAVIORS IN ADDICTION COUNSELING BY EXPLOITING CLASS CONFUSIONS.

Zhuohao Chen, Karan Singla, James Gibson, Dogan Can, Zac E Imel, David C Atkins, Panayiotis Georgiou, Shrikanth Narayanan

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Towards End-2-end Learning for Predicting Behavior Codes from Spoken Utterances in Psychotherapy Conversations.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Zhuohao ChenSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
Karan SinglaSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
James GibsonSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
Dogan CanSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
Zac E ImelDepartment Educational Psychology, University of Utah, Salt Lake City, UT, USA.
David C AtkinsDepartment of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.
Panayiotis GeorgiouSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
Shrikanth NarayananSignal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
University of Southern California · USUniversity of Utah · USUniversity of Washington · US

Funding

Implementation of Technology-Based Evaluation of Motivational InterviewingR01AA018673 · NIAAA · UNIVERSITY OF WASHINGTON · PI ATKINS, DAVID CHARLES, NARAYANAN, SHRIKANTH · 2010 to 2020
$5.9M
NIAAA NIH HHS R01 AA018673
6 · The paper itself

Abstract

In this work we address the problem of joint prosodic and lexical behavioral annotation for addiction counseling. We expand on past work that employed Recurrent Neural Networks (RNNs) on multimodal features by grouping and classifying subsets of classes. We propose two implementations: One is hierarchical classification, which uses the behavior confusion matrix to cluster similar classes and makes the prediction based on a tree structure. The second is a graph-based method which uses the result of the original classification just to find a certain subset of the most probable candidate classes, where the candidate sets of different predicted classes are determined by the class confusions. We make a second prediction with simpler classifier to discriminate the candidates. The evaluation shows that the strict hierarchical approach degrades performance, likely due to error propagation, while the graph-based hierarchy provides significant gains.

Indexed as

behavioral signal processingclass confusionsclass hierarchygraph-basedmultimodal

Identifiers

PMID36704712
PMCPMC9875731
OpenAlexW2939469531

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.