Predicting Therapy Success For Treatment as Usual and Blended Treatment in the Domain of Depression
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In: Internet Interventions, Vol. 12, 01.06.2018, p. 100-104.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Predicting Therapy Success For Treatment as Usual and Blended Treatment in the Domain of Depression
AU - van Breda, W.R.J.
AU - Bremer, Vincent
AU - Becker, Dennis
AU - Funk, Burkhardt
AU - Ruwaard, Jereon
AU - Riper, Heleen
N1 - Publisher Copyright: © 2017 The Authors
PY - 2018/6/1
Y1 - 2018/6/1
N2 - In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.
AB - In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.
KW - Business informatics
KW - Prediction
KW - Therapy success
KW - E-health
KW - Depression
KW - Classification
UR - http://www.scopus.com/inward/record.url?scp=85029568865&partnerID=8YFLogxK
U2 - 10.1016/j.invent.2017.08.003
DO - 10.1016/j.invent.2017.08.003
M3 - Journal articles
C2 - 29862165
VL - 12
SP - 100
EP - 104
JO - Internet Interventions
JF - Internet Interventions
SN - 2214-7829
ER -