Ortiz Abigail, Bradler Kamil, Hintze Arend
Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada.
Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON, Canada.
Bipolar Disord. 2018 Jan 22. doi: 10.1111/bdi.12603.
Bipolar disorder is a severe mood disorder characterized by alternating episodes of mania and depression. Several interventions have been developed to decrease high admission rates and high suicides rates associated with the illness, including psychoeducation and early episode detection, with mixed results. More recently, machine learning approaches have been used to aid clinical diagnosis or to detect a particular clinical state; however, contradictory results arise from confusion around which of the several automatically generated data are the most contributory and useful to detect a particular clinical state. Our aim for this study was to apply machine learning techniques and nonlinear analyses to a physiological time series dataset in order to find the best predictor for forecasting episodes in mood disorders.
We employed three different techniques: entropy calculations and two different machine learning approaches (genetic programming and Markov Brains as classifiers) to determine whether mood, energy or sleep was the best predictor to forecast a mood episode in a physiological time series.
Evening energy was the best predictor for both manic and depressive episodes in each of the three aforementioned techniques. This suggests that energy might be a better predictor than mood for forecasting mood episodes in bipolar disorder and that these particular machine learning approaches are valuable tools to be used clinically.
Energy should be considered as an important factor for episode prediction. Machine learning approaches provide better tools to forecast episodes and to increase our understanding of the processes that underlie mood regulation.
双相情感障碍是一种严重的情绪障碍，其特征为躁狂和抑郁发作交替出现。已经开发了多种干预措施来降低与该疾病相关的高住院率和高自杀率，包括心理教育和早期发作检测，但结果不一。最近，机器学习方法已被用于辅助临床诊断或检测特定的临床状态；然而，由于围绕几个自动生成的数据中哪一个对检测特定临床状态最具贡献性和有用性存在混淆，导致出现了相互矛盾的结果。我们这项研究的目的是将机器学习技术和非线性分析应用于生理时间序列数据集，以便找到预测情绪障碍发作的最佳预测指标。
我们采用了三种不同的技术：熵计算和两种不同的机器学习方法（遗传编程和马尔可夫脑作为分类器），以确定情绪、精力或睡眠是否是预测生理时间序列中情绪发作的最佳预测指标。
在上述三种技术中的每一种中，傍晚精力都是躁狂和抑郁发作的最佳预测指标。这表明，对于预测双相情感障碍中的情绪发作，精力可能比情绪是更好的预测指标，并且这些特定的机器学习方法是临床上可使用的有价值工具。
精力应被视为发作预测的一个重要因素。机器学习方法为预测发作和增进我们对情绪调节潜在过程的理解提供了更好的工具。
