Internet-delivered psychological treatments (IDPT) are seen as an effective and scalable pathway to improving the accessibility of mental healthcare. Within this context, treatment adherence is an especially pertinent challenge to address due to the reduced interaction between healthcare professionals and patients. In parallel, the increase in regulations surrounding the use of personal data, such as the General Data Protection Regulation (GDPR), makes data minimization a core consideration for real-world implementation of IDPTs. Consequently, this work proposes a Self-Attention-based deep learning approach to perform automatic adherence forecasting, while only relying on minimally sensitive login/logout-timestamp data. This approach was tested on a dataset containing 342 patients undergoing Guided Internet-delivered Cognitive Behavioral Therapy (G-ICBT) treatment. Of these 342 patients, 101 (~30%) were considered non-adherent (dropout) based on the adherence definition used in this work (i.e. at least eight connections to the platform lasting more than a minute over 56 days). The proposed model achieved over 70% average balanced accuracy, after only 20 out of the 56 days (~1/3) of the treatment had elapsed. This study demonstrates that automatic adherence forecasting for G-ICBT, is achievable using only minimally sensitive data, thus facilitating the implementation of such tools within real-world IDPT platforms.
翻译:互联网提供的心理治疗(IDPT)被认为是改善精神保健的可获取性的有效和可扩展的途径,在这方面,由于保健专业人员和病人之间互动减少,坚持治疗是应对一项特别相关的挑战,同时,围绕个人数据使用条例的增加,如《一般数据保护条例》,使数据最小化成为实际实施《互联网提供心理治疗》的核心考虑因素。因此,这项工作建议采取基于自我意识的深层次学习方法,进行自动遵守预测,同时只依靠最低敏感登录/登录时间戳破数据。这一方法在一套数据集中测试了342名正在指导互联网提供 Cognitive behavioral治疗的病人。在这342名病人中,101人(~30%)被视为非适应性(放弃),这是根据这项工作中使用的遵守定义(即与平台的至少8个连接超过56天的分钟)进行自动遵守性预测。拟议的模型在56天后,仅用56天之内(~1/3)在指导互联网提供协同治疗的342名病人的数据集中,因此只能使用可实现的实时预测工具。