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 -