Search research articles
Contact Us
Filters
Showing results (1-10 of 33) with videos related to
Page
of 4
Sort By:
Depression Research and Treatment
|
February 9, 2019
Heterogeneity Matters: Predicting Self-Esteem in Online Interventions Based on Ecological Momentary Assessment Data
Vincent Bremer, Burkhardt Funk, Heleen Riper
Frontiers in Psychology
|
February 10, 2023
Editorial: Quantitative modeling of psychopathology using passively collected data
Nicholas C Jacobson, Burkhardt Funk, Saeed Abdullah
Digital Health
|
May 17, 2024
Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions
Kirsten Zantvoort, Nils Hentati Isacsson, Burkhardt Funk, et al.
Proceedings of the National Academy of Sciences of the United States of America
|
August 1, 2023
Understanding the first-offer conundrum: How buyer offers impact sale price and impasse risk in 26 million eBay negotiations
Martin Schweinsberg, Hannes M Petrowsky, Burkhardt Funk, et al.
Journal of Healthcare Informatics Research
|
November 6, 2023
Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions
Kirsten Zantvoort, Jonas Scharfenberger, Leif Boß, et al.
Frontiers in Digital Health
|
June 7, 2023
Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms
Silvan Hornstein, Kirsten Zantvoort, Ulrike Lueken, et al.
Evidence-Based Mental Health
|
February 13, 2020
Evaluation of a temporal causal model for predicting the mood of clients in an online therapy
Dennis Becker, Vincent Bremer, Burkhardt Funk, et al.
NPJ Digital Medicine
|
December 18, 2024
Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions
Kirsten Zantvoort, Barbara Nacke, Dennis Görlich, et al.
Journal of Medical Internet Research
|
October 28, 2020
Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach
Vincent Bremer, Philip I Chow, Burkhardt Funk, et al.
Internet Interventions
|
August 24, 2018
Predictive modeling in e-mental health: A common language framework
Dennis Becker, Ward van Breda, Burkhardt Funk, et al.
Page
of 4
Search research articles
Search
Showing results (1-10 of 33) with videos related to
Sort By:
Page
of 4
Depression Research and Treatment
|
February 9, 2019
Heterogeneity Matters: Predicting Self-Esteem in Online Interventions Based on Ecological Momentary Assessment Data
Vincent Bremer, Burkhardt Funk, Heleen Riper
Frontiers in Psychology
|
February 10, 2023
Editorial: Quantitative modeling of psychopathology using passively collected data
Nicholas C Jacobson, Burkhardt Funk, Saeed Abdullah
Digital Health
|
May 17, 2024
Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions
Kirsten Zantvoort, Nils Hentati Isacsson, Burkhardt Funk, et al.
Proceedings of the National Academy of Sciences of the United States of America
|
August 1, 2023
Understanding the first-offer conundrum: How buyer offers impact sale price and impasse risk in 26 million eBay negotiations
Martin Schweinsberg, Hannes M Petrowsky, Burkhardt Funk, et al.
Journal of Healthcare Informatics Research
|
November 6, 2023
Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions
Kirsten Zantvoort, Jonas Scharfenberger, Leif Boß, et al.
Frontiers in Digital Health
|
June 7, 2023
Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms
Silvan Hornstein, Kirsten Zantvoort, Ulrike Lueken, et al.
Evidence-Based Mental Health
|
February 13, 2020
Evaluation of a temporal causal model for predicting the mood of clients in an online therapy
Dennis Becker, Vincent Bremer, Burkhardt Funk, et al.
NPJ Digital Medicine
|
December 18, 2024
Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions
Kirsten Zantvoort, Barbara Nacke, Dennis Görlich, et al.
Journal of Medical Internet Research
|
October 28, 2020
Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach
Vincent Bremer, Philip I Chow, Burkhardt Funk, et al.
Internet Interventions
|
August 24, 2018
Predictive modeling in e-mental health: A common language framework
Dennis Becker, Ward van Breda, Burkhardt Funk, et al.
Page
of 4