A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
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DOI：
10.1038/s41398-018-0334-0
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发表时间：
2018-12-13
影响因子：
6.8
通讯作者：
Saunders, Kate E. A.
中科院分区：
文献类型：
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作者：
Arribas, Imanol Perez;Goodwin, Guy M.;Saunders, Kate E. A.
移动技术为对长期健康状况进行前瞻性、高分辨率监测提供了新的机会。在精神病学中，这种机会似乎特别有希望，因为精神病学的诊断往往依赖于对情绪状态的回顾性和主观回忆。然而，从这些技术提供的复杂时间序列数据中获得临床有意义的信息是具有挑战性的，并且目前对患者护理的影响是不确定的。在这项研究中，130名患有双相情感障碍（n - 48）或边缘性人格障碍（n - 31）的参与者和健康志愿者（n = 51）使用定制的智能手机应用程序完成了长达1年的每日情绪评估。一种基于特征的学习方法被用来捕捉情绪不同元素之间不断演变的相互关系，并利用这些信息对参与者的诊断进行分类，并预测随后的情绪。使用签名方法可以根据自我报告的情绪来区分三个参与者组。该方法将75%的参与者分类为正确的诊断组，而使用标准方法的比例为54%。随后的情绪评分被正确预测，准确率达到70%。与双相情感障碍（82-90%）和边缘型人格障碍（70-78%）相比，健康志愿者的情绪预测（89-98%）最为准确。该方法在诊断分类和预测未来情绪方面为情绪数据分析提供了一种有效的方法。它还强调了不同的可预测性和疾病内部固有的重叠。这三个队列在他们的报告中提供了内部一致但不同的情绪相互作用模式，这有可能实现更有效和准确的诊断，从而早期治疗。
Mobile technologies offer new opportunities for prospective, high resolution monitoring of long-term health conditions. The opportunities seem of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, deriving clinically meaningful information from the complex time series data these technologies present is challenging, and the current implications for patient care are uncertain. In this study, 130 participants with bipolar disorder (n - 48) or borderline personality disorder (n - 31) and healthy volunteers (n = 51) completed daily mood ratings using a bespoke smartphone app for up to 1 year. A signature-based learning method was used to capture the evolving interrelationships between the different elements of mood and exploit this information to classify participants' diagnosis and to predict subsequent mood. The three participant groups could be distinguished from one another on the basis of self-reported mood using the signature methodology. The methodology classified 75% of participants into the correct diagnostic group compared with 54% using standard approaches. Subsequent mood ratings were correctly predicted with >70% accuracy. Prediction of mood was most accurate in healthy volunteers (89-98%) compared to bipolar disorder (82-90%) and borderline personality disorder (70-78%). The signature method provided an effective approach to the analysis of mood data both in terms of diagnostic classification and prediction of future mood. It also highlighted the differing predictability and the overlap inherent within disorders. The three cohorts offered internally consistent but distinct patterns of mood interaction in their reporting which have the potential to enable more efficient and accurate diagnoses and thus earlier treatment.