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Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia

机译：使用行为节奏和多任务学习预测精神分裂症的细粒度症状

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Schizophrenia is a severe and complex psychiatric disorder with heterogeneous and dynamic multi-dimensional symptoms. Behavioral rhythms, such as sleep rhythm, are usually disrupted in people with schizophrenia. As such, behavioral rhythm sensing with smartphones and machine learning can help better understand and predict their symptoms. Our goal is to predict fine-grained symptom changes with interpretable models. We computed rhythm-based features from 61 participants with 6,132 days of data and used multi-task learning to predict their ecological momentary assessment scores for 10 different symptom items. By taking into account both the similarities and differences between different participants and symptoms, our multi-task learning models perform statistically significantly better than the models trained with single-task learning for predicting patients’ individual symptom trajectories, such as feeling depressed, social, and calm and hearing voices. We also found different subtypes for each of the symptoms by applying unsupervised clustering to the feature weights in the models. Taken together, compared to the features used in the previous studies, our rhythm features not only improved models’ prediction accuracy but also provided better interpretability for how patients’ behavioral rhythms and the rhythms of their environments influence their symptom conditions. This will enable both the patients and clinicians to monitor how these factors affect a patient’s condition and how to mitigate the influence of these factors. As such, we envision that our solution allows early detection and early intervention before a patient’s condition starts deteriorating without requiring extra effort from patients and clinicians.
机译：精神分裂症是一种严重和复杂的精神疾病，具有异质和动态的多维症状。诸如睡眠节律的行为节律通常被精神分裂症的人类中断。因此，使用智能手机和机器学习的行为节奏可以帮助更好地理解并预测其症状。我们的目标是通过可解释的模型预测细粒度的症状变化。我们从61名参与者计算了基于节奏的特征，并使用了6132天的数据，并使用了多任务学习，以预测其10种不同症状项目的生态瞬间评估分数。通过考虑不同参与者和症状之间的相似之处和差异，我们的多任务学习模型比用单任务学习培训的模型来表现出统计上显着更好地更好地预测患者个体症状轨迹，例如感到抑郁，社会和社会冷静和听力的声音。我们还通过将无监督的聚类应用于模型中的特征权重来找到每个症状的不同亚型。与上一项研究中使用的特征相比，我们的节奏不仅改善了模型的预测准确性，而且还为患者的行为节奏和其环境的节奏提供了更好的解释性，影响其症状条件。这将使患者和临床医生能够监测这些因素如何影响患者的病症以及如何减轻这些因素的影响。因此，我们设想我们的解决方案允许在患者的病情开始恶化之前早期检测和早期干预，而无需患者和临床医生的额外努力。

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