Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study.
Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study.
复制标题
DOI：
10.1186/s12888-022-03923-1
复制
发表时间：
2022-04-22
期刊：
影响因子：
4.4
通讯作者：
Mulsant, Benoit H.
中科院分区：
文献类型：
--
作者：
Ortiz, Abigail;Hintze, Arend;Burnett, Rachael;Gonzalez-Torres, Christina;Unger, Samantha;Yang, Dandan;Miao, Jingshan;Alda, Martin;Mulsant, Benoit H.
精神障碍或行为的预测模型（例如，自杀）已成功地在人群水平上开发，但目前的人口统计学和临床变量既不敏感，也不具体，足以作出个人的临床预测。预测疾病发作在双相情感障碍（BD）中尤其重要，BD是一种具有高复发率、残疾率和自杀率的情绪障碍。因此，为了了解BD中事件生成所涉及的动态变化，我们建议使用被动传感，非线性技术和深度异常检测来提取和解释暗示复发的个体疾病轨迹和模式。在这里，我们描述了我们设计来测试这一假设的研究及其设计的基本原理。这是一项在200例成人BD患者中进行的非接触式队列研究方案。参与者将被随访长达2年，在此期间，他们将使用被动传感进行连续监测，被动传感是一种可穿戴设备，可收集多模态生理（心率变异性）和客观（睡眠，活动）数据。参与者将完成㈠全面基线评估; ㈡每周评估; ㈢使用电子评级量表进行的每日评估。将使用非线性技术和深度异常检测来分析数据，以预测疾病发作。这项非接触式大型队列研究旨在获得并联合收割机结合高维、多模态生理、客观和主观数据。我们的工作，通过概念化的情绪作为一个动态的生物系统的属性，将证明将个体变异性的模型通知临床轨迹和预测复发BD的可行性。
Predictive models for mental disorders or behaviors (e.g., suicide) have been successfully developed at the level of populations, yet current demographic and clinical variables are neither sensitive nor specific enough for making individual clinical predictions. Forecasting episodes of illness is particularly relevant in bipolar disorder (BD), a mood disorder with high recurrence, disability, and suicide rates. Thus, to understand the dynamic changes involved in episode generation in BD, we propose to extract and interpret individual illness trajectories and patterns suggestive of relapse using passive sensing, nonlinear techniques, and deep anomaly detection. Here we describe the study we have designed to test this hypothesis and the rationale for its design. This is a protocol for a contactless cohort study in 200 adult BD patients. Participants will be followed for up to 2 years during which they will be monitored continuously using passive sensing, a wearable that collects multimodal physiological (heart rate variability) and objective (sleep, activity) data. Participants will complete (i) a comprehensive baseline assessment; (ii) weekly assessments; (iii) daily assessments using electronic rating scales. Data will be analyzed using nonlinear techniques and deep anomaly detection to forecast episodes of illness. This proposed contactless, large cohort study aims to obtain and combine high-dimensional, multimodal physiological, objective, and subjective data. Our work, by conceptualizing mood as a dynamic property of biological systems, will demonstrate the feasibility of incorporating individual variability in a model informing clinical trajectories and predicting relapse in BD.
登录
查看更多内容
影响因子：
--
作者：
Judd, LL;Akiskal, HS;Keller, MB
通讯作者：
Keller, MB
影响因子：
7.4
作者：
Carroll JK;Moorhead A;Bond R;LeBlanc WG;Petrella RJ;Fiscella K
通讯作者：
Fiscella K
影响因子：
5.4
作者：
Ortiz, Abigail;Bradler, Kamil;Alda, Martin
通讯作者：
Alda, Martin
影响因子：
22.4
作者：
Franklin, Joseph C.;Ribeiro, Jessica D.;Nock, Matthew K.
通讯作者：
Nock, Matthew K.
影响因子：
10.6
作者：
Altman, EG;Hedeker, D;Davis, JM
通讯作者：
Davis, JM