Literature DB >> 32046990

Evaluation of a temporal causal model for predicting the mood of clients in an online therapy.

Dennis Becker1, Vincent Bremer2, Burkhardt Funk2, Mark Hoogendoorn3, Artur Rocha4, Heleen Riper5.   

Abstract

Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Keywords:  mood prediction; online treatment; predictive modelling; temporal causal model

Mesh:

Year:  2020        PMID: 32046990     DOI: 10.1136/ebmental-2019-300135

Source DB:  PubMed          Journal:  Evid Based Ment Health        ISSN: 1362-0347


  1 in total

1.  Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow.

Authors:  John Torous; Keris Jän Myrick; Natali Rauseo-Ricupero; Joseph Firth
Journal:  JMIR Ment Health       Date:  2020-03-26
  1 in total

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