While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.

中文翻译：

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用于预测明天情绪、压力和健康的个性化多任务学习

虽然准确预测情绪和幸福感可能具有许多重要的临床益处，但传统的机器学习 (ML) 方法在该领域往往表现不佳。我们认为这是因为一刀切的机器学习模型本质上不适合预测情绪和压力等结果，这些结果因个体差异而有很大差异。因此，我们采用多任务学习 (MTL) 技术来训练个性化 ML 模型，这些模型根据每个人的需求进行定制，但仍然利用来自整个人群的数据。比较了 MTL 的三种公式： i) MTL 深度神经网络，它共享几个隐藏层，但每个任务都有独特的最终层；ii) 多任务多核学习，通过特征类型的核权重跨任务提供信息；iii) 一个分层贝叶斯模型，其中任务共享一个共同的狄利克雷过程先验。我们以开源形式提供这项工作的代码。这些技术是在使用从调查、可穿戴传感器、智能手机日志和天气中收集的数据来预测未来情绪、压力和健康的背景下进行研究的。实证结果表明，与传统机器学习方法相比，使用 MTL 来解释个体差异提供了巨大的性能改进，并提供了个性化的、可操作的见解。