Data Processing (What Works When for Whom)
Data processing scripts for the project What Works When for Whom
Advancing therapy change process research
Mental illnesses, like depression and anxiety, are among the leading causes of the global burden of disease. E-mental health (EMH) interventions, such as web-based psychotherapy treatments, are increasingly used to improve access to psychotherapy for a wider audience. Whereas different EMH interventions tend to be equally effective, the responsiveness to a specific treatment shows large individual differences. The personalization of treatments is seen as the major road for improvement.
eScience methods and tools
Because most EMH interventions use language for communication between counselors and clients, assessing language use provides an important avenue for opening the black box of what happens within therapy. EMH also makes data of the linguistic interactions between client and counselor available on an unprecedented large scale.
The objective of this interdisciplinary project is to use eScience methods and tools, in particular natural language processing, visualization and multivariate analysis methods, to analyze patterns in therapy-related textual features in e-mail correspondence between counselor and client.
Improving the effectiveness of EMH
By connecting patterns of known change indicators to therapy outcome, the question What Works When for Whom? can be answered, which will greatly improve the effectiveness of EMH. The core of the project concerns the development of integrated, modular software for the Dutch language, using data from six EMH-interventions with a total of 10.000 e-mails. These data are sufficiently large and varied to allow for computer-based modelling, and testing of use cases with varying complexity. At the end of the project, the step toward English language software will be made to increase the impact of the project.
The COVID-19 Pandemic as a Use Case
Automated video-based movement assessment using machine learning to support personalized treatment of movement disorders
Early prediction of dyslexia in infants using machine learning
An alternative approach for intelligent systems to understand human speech
Sharing TADPOLE’s algorithms for reuse and evaluation
Advancing technology for multimodal analysis of emotion expression in everyday life
Medical experts helping machines diagnose
Data processing scripts for the project What Works When for Whom
A flexible solution to build text mining workflows that allows you to quickly combine Natural Language Processing tools from different sources.
Add-on providing quantitative textual story analysis based on principles in narrative psychology
Orange modules for text processing in the project What Works When for Whom
Create CWL workflows by writing a simple Python script.