van Breda Ward, Bremer Vincent, Becker Dennis, Hoogendoorn Mark, Funk Burkhardt, Ruwaard Jeroen, Riper Heleen
Department of Computer Science, VU University, Amsterdam, The Netherlands.
Institute of Information Systems, Leuphana University, Luneburg, Germany.
Internet Interv. 2018 Jun;12:100-104. doi: 10.1016/j.invent.2017.08.003.
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.
在本文中，我们探讨了预测精神卫生保健患者治疗成功可能性的潜力。此类预测最终可改善将有效治疗类型与个体进行匹配的过程。在欧盟项目E - COMPARED中，收集了有关抑郁症患者的各种信息。我们使用这些数据（其中276名患者接受常规治疗，227名患者接受混合治疗）来研究我们在多大程度上能够预测治疗成功。为此，我们采用不同的编码策略进行预处理，运用不同的特征选择技术，并采用不同的统计程序。结果发现，对于常规治疗，平均AUC值高达0.7628时具有显著的预测能力；对于混合治疗，平均AUC值高达0.7765时具有显著的预测能力。目前添加混合治疗的每日评估数据并不能提高预测准确性。需要进行成本效益分析以确定在实际应用中的附加潜力。
