Mikus Adam, Hoogendoorn Mark, Rocha Artur, Gama Joao, Ruwaard Jeroen, Riper Heleen
Vrije Universiteit Amsterdam, Department of Computer Science, De Boelelaan 1081, Amsterdam 1081 HV, The Netherlands.
Centre for Information Systems and Computer Graphics, INESC TEC, Porto, Portugal.
Internet Interv. 2017 Oct 7;12:105-110. doi: 10.1016/j.invent.2017.10.001. eCollection 2018 Jun.
Technology driven interventions provide us with an increasing amount of fine-grained data about the patient. This data includes regular ecological momentary assessments (EMA) but also response times to EMA questions by a user. When observing this data, we see a huge variation between the patterns exhibited by different patients. Some are more stable while others vary a lot over time. This poses a challenging problem for the domain of artificial intelligence and makes on wondering whether it is possible to predict the future mental state of a patient using the data that is available. In the end, these predictions could potentially contribute to interventions that tailor the feedback to the user on a daily basis, for example by warning a user that a fall-back might be expected during the next days, or by applying a strategy to prevent the fall-back from occurring in the first place. In this work, we focus on short term mood prediction by considering the adherence and usage data as an additional predictor. We apply recurrent neural networks to handle the temporal aspects best and try to explore whether individual, group level, or one single predictive model provides the highest predictive performance (measured using the root mean squared error (RMSE)). We use data collected from patients from five countries who used the ICT4Depression/MoodBuster platform in the context of the EU E-COMPARED project. In total, we used the data from 143 patients (with between 9 and 425 days of EMA data) who were diagnosed with a major depressive disorder according to DSM-IV. Results show that we can make predictions of short term mood change quite accurate (ranging between 0.065 and 0.11). The past EMA mood ratings proved to be the most influential while adherence and usage data did not improve prediction accuracy. In general, group level predictions proved to be the most promising, however differences were not significant. Short term mood prediction remains a difficult task, but from this research we can conclude that sophisticated machine learning algorithms/setups can result in accurate performance. For future work, we want to use more data from the mobile phone to improve predictive performance of short term mood.
技术驱动的干预措施为我们提供了越来越多关于患者的细粒度数据。这些数据包括定期的生态瞬时评估（EMA），也包括用户对EMA问题的响应时间。在观察这些数据时，我们发现不同患者呈现的模式存在巨大差异。有些患者的模式更稳定，而另一些患者则随时间变化很大。这给人工智能领域带来了一个具有挑战性的问题，让人不禁思考是否有可能利用现有的数据预测患者未来的心理状态。最终，这些预测可能有助于采取一些干预措施，以便每天根据用户情况定制反馈，例如警告用户未来几天可能会出现情绪回落，或者首先应用一种策略来防止情绪回落的发生。在这项工作中，我们将依从性和使用数据作为额外的预测指标，专注于短期情绪预测。我们应用递归神经网络来最好地处理时间因素，并尝试探索个体、群体层面或单一预测模型是否能提供最高的预测性能（使用均方根误差（RMSE）来衡量）。我们使用了在欧盟E-COMPARED项目背景下，从五个国家使用ICT4Depression/MoodBuster平台的患者那里收集的数据。总共，我们使用了143名患者的数据（EMA数据时长在9至425天之间），这些患者根据《精神疾病诊断与统计手册》第四版（DSM-IV）被诊断为重度抑郁症。结果表明，我们能够相当准确地预测短期情绪变化（范围在0.065至0.11之间）。过去的EMA情绪评分被证明是最具影响力的，而依从性和使用数据并没有提高预测准确性。总体而言，群体层面的预测被证明最有前景，不过差异并不显著。短期情绪预测仍然是一项艰巨的任务，但从这项研究中我们可以得出结论，复杂的机器学习算法/设置可以带来准确的性能表现。对于未来的工作，我们希望使用更多来自手机的数据来提高短期情绪的预测性能。
