Spitzer Michael, Dattner Itai, Zilcha-Mano Sigal
Department of Psychology, University of Haifa, Haifa, Israel.
Department of Statistics, University of Haifa, Haifa, Israel.
Front Psychiatry. 2023 Mar 13;14:1082598. doi: 10.3389/fpsyt.2023.1082598. eCollection 2023.
Science faces challenges in developing much-needed precision mental health treatments to accurately identify and diagnose mental health problems and the optimal treatment for each individual. Digital twins (DTs) promise to revolutionize the field of mental health, as they are doing in other fields of science, including oncology and cardiology, where they have been successfully deployed. The use of DTs in mental health is yet to be explored. In this Perspective, we lay the conceptual foundations for mental health DTs (MHDT). An MHDT is a virtual representation of an individual's mental states and processes. It is continually updated from data collected over the lifespan of the individual, and guides mental health professionals in diagnosing and treating patients based on mechanistic models and statistical and machine learning tools. The merits of MHDT are demonstrated through the example of the working alliance between the therapist and the patient, which is one of the most consistent mechanisms predicting treatment outcome.
在开发急需的精准心理健康治疗方法以准确识别和诊断心理健康问题以及为每个人提供最佳治疗方案方面,科学面临着挑战。数字孪生(DTs)有望彻底改变心理健康领域,就像它们在包括肿瘤学和心脏病学在内的其他科学领域所做的那样,这些领域已经成功应用了数字孪生。数字孪生在心理健康领域的应用尚待探索。在这篇观点文章中,我们为心理健康数字孪生(MHDT)奠定了概念基础。心理健康数字孪生是个体心理状态和过程的虚拟表示。它根据个体一生中收集的数据不断更新,并基于机制模型以及统计和机器学习工具,指导心理健康专业人员对患者进行诊断和治疗。通过治疗师与患者之间的工作联盟这一例子,展示了心理健康数字孪生的优点,工作联盟是预测治疗结果最一致的机制之一。