Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Schizophr Bull. 2019 Mar 7;45(2):272-276. doi: 10.1093/schbul/sby171.
The rapid rise and now widespread distribution of handheld and wearable devices, such as smartphones, fitness trackers, or smartwatches, has opened a new universe of possibilities for monitoring emotion and cognition in everyday-life context, and for applying experience- and context-specific interventions in psychosis. These devices are equipped with multiple sensors, recording channels, and app-based opportunities for assessment using experience sampling methodology (ESM), which enables to collect vast amounts of temporally highly resolved and ecologically valid personal data from various domains in daily life. In psychosis, this allows to elucidate intermediate and clinical phenotypes, psychological processes and mechanisms, and their interplay with socioenvironmental factors, as well as to evaluate the effects of treatments for psychosis on important clinical and social outcomes. Although these data offer immense opportunities, they also pose tremendous challenges for data analysis. These challenges include the sheer amount of time series data generated and the many different data modalities and their specific properties and sampling rates. After a brief review of studies and approaches to ESM and ecological momentary interventions in psychosis, we will discuss recurrent neural networks (RNNs) as a powerful statistical machine learning approach for time series analysis and prediction in this context. RNNs can be trained on multiple data modalities simultaneously to learn a dynamical model that could be used to forecast individual trajectories and schedule online feedback and intervention accordingly. Future research using this approach is likely going to offer new avenues to further our understanding and treatments of psychosis.
手持和可穿戴设备（如智能手机、健身追踪器或智能手表）的迅速普及，为在日常生活环境中监测情绪和认知以及在精神病学中应用基于经验和情境的干预措施开辟了新的可能性。这些设备配备了多个传感器、记录通道和基于应用程序的经验采样方法（ESM）评估机会，这使我们能够从日常生活的各个领域中收集大量时间分辨率高且生态有效的个人数据。在精神病学中，这可以阐明中间和临床表型、心理过程和机制，以及它们与社会环境因素的相互作用，还可以评估针对精神病的治疗对重要临床和社会结果的影响。虽然这些数据提供了巨大的机会，但它们也对数据分析提出了巨大的挑战。这些挑战包括生成的时间序列数据的数量巨大，以及不同的数据模式及其特定属性和采样率。在简要回顾了精神病学中的 ESM 和生态瞬时干预研究和方法之后，我们将讨论递归神经网络（RNN）作为一种强大的统计机器学习方法，用于该背景下的时间序列分析和预测。RNN 可以同时对多个数据模式进行训练，以学习一个动态模型，该模型可用于预测个体轨迹，并相应地安排在线反馈和干预。未来使用这种方法的研究很可能会为进一步理解和治疗精神病提供新途径。
