Tuarob Suppawong, Tucker Conrad S, Kumara Soundar, Giles C Lee, Pincus Aaron L, Conroy David E, Ram Nilam
Faculty of Information and Communication Technology, Mahidol University, Thailand.
Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
J Biomed Inform. 2017 Apr;68:1-19. doi: 10.1016/j.jbi.2017.02.010. Epub 2017 Feb 15.
It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in terms of both magnitude of error and directional accuracy. These results not only present a successful usage of machine learning techniques in psychological studies, but also serve as a building block for multiple medical applications that could rely on an automated system to gauge individuals' mental states.
人们认为，压力和焦虑等异常心理状态不仅会给个人带来痛苦，在某些极端情况下还会导致悲剧。预测个体当前和未来时间段的心理状态的能力对医疗从业者而言可能至关重要。目前，预测个体心理状态的实际方法是通过心理检查，即由心理专家进行评估。然而，此类方法可能耗费时间和资源，从而削弱了它们在广泛人群中的广泛适用性。此外，一些个体可能也未意识到自己的心理状态，或者在评估过程中可能不愿表达自己。因此，他们的异常心理状态可能会在很长一段时间内未被发现。这项工作的目标是展示使用基于先进机器学习的方法来生成预测个体当前和未来心理状态的数学模型的能力。心理状态预测问题被转化为时间序列预测问题，其中个体被表示为监测到的身体和行为属性的多变量时间序列流。然后自动生成一个个性化的数学模型来捕捉这些属性之间的依赖性，该模型用于预测每个个体的心理状态。具体而言，我们首先阐述传统多变量时间序列预测方法（如向量自回归）的缺点。然后，我们表明可以通过使用为捕捉时间序列数据中的时间依赖性而修改的机器学习回归技术来缓解此类问题。一项使用150名人类参与者数据的案例研究表明，就误差幅度和方向准确性而言，所提出的基于机器学习的预测方法比传统向量自回归模型更适用于高维心理数据。这些结果不仅展示了机器学习技术在心理学研究中的成功应用，也为多个可能依赖自动化系统来评估个体心理状态的医学应用奠定了基础。
