Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia
Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia
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DOI：
10.1038/s41598-020-71689-1
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发表时间：
2020-09-15
影响因子：
4.6
通讯作者：
Choudhury, Tanzeem
中科院分区：
文献类型：
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作者：
Tseng, Vincent W. -S.;Sano, Akane;Choudhury, Tanzeem
精神分裂症是一种严重而复杂的精神障碍，具有异质性和动态的多维症状。精神分裂症患者的行为节律，如睡眠节律，通常会被打乱。因此，智能手机和机器学习的行为节律感知可以帮助更好地理解和预测他们的症状。我们的目标是用可解释的模型预测细粒度的症状变化。我们计算了61名参与者的基于节奏的特征，获得了6,132天的数据，并使用多任务学习来预测他们对10个不同症状项目的生态瞬时评估分数。通过考虑不同参与者和症状之间的相似性和差异，我们的多任务学习模型在预测患者的个别症状轨迹方面，在统计上显著优于单任务学习模型，如感觉抑郁、社交、平静和听到声音。我们还通过对模型中的特征权重应用非监督聚类来发现每个症状的不同子类型。综上所述，与以前研究中使用的特征相比，我们的节奏特征不仅提高了模型的预测精度，而且还为患者的行为节奏和环境节奏如何影响他们的症状提供了更好的解释力。这将使患者和临床医生都能够监控这些因素如何影响患者的病情，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可以在患者病情开始恶化之前及早发现和早期干预，而不需要患者和临床医生付出额外的努力。
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.