205,490 research outputs found
Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder : Text Mining Study
Publisher Copyright: © 2022 Sanna Mylläri, Suoma Eeva Saarni, Ville Ritola.Background: Text mining methods such as topic modeling can offer valuable information on how and to whom internet-delivered cognitive behavioral therapies (iCBT) work. Although iCBT treatments provide convenient data for topic modeling, it has rarely been used in this context. Objective: Our aims were to apply topic modeling to written assignment texts from iCBT for generalized anxiety disorder and explore the resulting topics' associations with treatment response. As predetermining the number of topics presents a considerable challenge in topic modeling, we also aimed to explore a novel method for topic number selection. Methods: We defined 2 latent Dirichlet allocation (LDA) topic models using a novel data-driven and a more commonly used interpretability-based topic number selection approaches. We used multilevel models to associate the topics with continuous-valued treatment response, defined as the rate of per-session change in GAD-7 sum scores throughout the treatment. Results: Our analyses included 1686 patients. We observed 2 topics that were associated with better than average treatment response: "well-being of family, pets, and loved ones"from the data-driven LDA model (B=-0.10 SD/session/Δtopic; 95% CI -016 to -0.03) and "children, family issues"from the interpretability-based model (B=-0.18 SD/session/Δtopic; 95% CI -0.31 to -0.05). Two topics were associated with worse treatment response: "monitoring of thoughts and worries"from the data-driven model (B=0.06 SD/session/Δtopic; 95% CI 0.01 to 0.11) and "internet therapy"from the interpretability-based model (B=0.27 SD/session/Δtopic; 95% CI 0.07 to 0.46). Conclusions: The 2 LDA models were different in terms of their interpretability and broadness of topics but both contained topics that were associated with treatment response in an interpretable manner. Our work demonstrates that topic modeling is well suited for iCBT research and has potential to expose clinically relevant information in vast text data.Peer reviewe
Jointly Modeling Topics and Intents with Global Order Structure
Modeling document structure is of great importance for discourse analysis and
related applications. The goal of this research is to capture the document
intent structure by modeling documents as a mixture of topic words and
rhetorical words. While the topics are relatively unchanged through one
document, the rhetorical functions of sentences usually change following
certain orders in discourse. We propose GMM-LDA, a topic modeling based
Bayesian unsupervised model, to analyze the document intent structure
cooperated with order information. Our model is flexible that has the ability
to combine the annotations and do supervised learning. Additionally, entropic
regularization can be introduced to model the significant divergence between
topics and intents. We perform experiments in both unsupervised and supervised
settings, results show the superiority of our model over several
state-of-the-art baselines.Comment: Accepted by AAAI 201
Point-occurrence self-similarity in crackling-noise systems and in other complex systems
It has been recently found that a number of systems displaying crackling
noise also show a remarkable behavior regarding the temporal occurrence of
successive events versus their size: a scaling law for the probability
distributions of waiting times as a function of a minimum size is fulfilled,
signaling the existence on those systems of self-similarity in time-size. This
property is also present in some non-crackling systems. Here, the uncommon
character of the scaling law is illustrated with simple marked renewal
processes, built by definition with no correlations. Whereas processes with a
finite mean waiting time do not fulfill a scaling law in general and tend
towards a Poisson process in the limit of very high sizes, processes without a
finite mean tend to another class of distributions, characterized by double
power-law waiting-time densities. This is somehow reminiscent of the
generalized central limit theorem. A model with short-range correlations is not
able to escape from the attraction of those limit distributions. A discussion
on open problems in the modeling of these properties is provided.Comment: Submitted to J. Stat. Mech. for the proceedings of UPON 2008 (Lyon),
topic: crackling nois
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Originally designed to model text, topic modeling has become a powerful tool
for uncovering latent structure in domains including medicine, finance, and
vision. The goals for the model vary depending on the application: in some
cases, the discovered topics may be used for prediction or some other
downstream task. In other cases, the content of the topic itself may be of
intrinsic scientific interest.
Unfortunately, even using modern sparse techniques, the discovered topics are
often difficult to interpret due to the high dimensionality of the underlying
space. To improve topic interpretability, we introduce Graph-Sparse LDA, a
hierarchical topic model that leverages knowledge of relationships between
words (e.g., as encoded by an ontology). In our model, topics are summarized by
a few latent concept-words from the underlying graph that explain the observed
words. Graph-Sparse LDA recovers sparse, interpretable summaries on two
real-world biomedical datasets while matching state-of-the-art prediction
performance
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