Multimodal monitoring and high-dimensional data for episode prediction in bipolar disorder

摘要 双相情感障碍(BD)是一种复发率和致残率高、经济负担重、 据估计，自杀风险是普通人群的20倍。而有效的治疗方法是 如果有，BD患者一生中有很大一部分时间是有症状的。预测疾病发作的时间 是降低自杀率和伤残率并优化医疗成本的一项有价值的战略。 这项(R21)探索性/发展性研究的总体目标是获得试点数据以支持 一种新的方法预测稳定的成人慢性阻塞性肺疾病患者情绪发作的可行性和潜在价值 BD。该提案旨在开发新的数据建模和推理技术，以实现更多 量身定制的临床信号检测：随着时间的推移，检查每个人体内的变化。为了做到这一点，我们 提出将多通道、高维远程监测数据、非线性技术和人工智能相结合 情报分类系统。这一方法建立在我们在以下方面的初步工作之上：(I)非线性 研究BD情绪调节的技术；(Ii)获奖的机器模拟 用于BD发作预测的学习技术(马尔可夫脑)。 目标：目标1(可行性)：获取和整合多模式数据，以进行时间序列分析和 目的：计算90例心境良好的BD成人的熵水平。探索性目标2(潜在价值)：利用 机器学习技术(马尔可夫大脑)，以区分抑郁或抑郁风险较高的参与者 基于他们的时间序列和熵水平的躁狂复发(来自目标1)。 假设：H1：我们将能够在80%的参与者中收集足够的数据并整合 用于执行时间序列分析和计算熵值的多模式数据。H2：马尔可夫大脑将 根据高(低)自相关时间序列确定情绪发作风险较高的参与者 和低(与高)的熵水平。 意义：此R21应用程序通过以下方式挑战更传统的预测模型 将个体间和个体内的变异性概念化为生物系统的一种动态属性。通过 利用密集抽样的客观和主观数据、自主、临床和人口统计数据，这 该提案旨在开发推理技术，以检查每个人内部的变化，超过 时间，以提高估计性能。最终，如果我们发展预测能力 情绪发作，我们应该能够预防它们。

SUMMARY Bipolar disorder (BD) is a mood disorder with high recurrence and disability rates, high economic burden, and an estimated suicide risk 20 times higher than the general population. While efficacious treatment is available, BD patients spend a large proportion of their life symptomatic. Predicting the onset of episodes is a valuable strategy to decrease suicide and disability rates and to optimize healthcare costs. The overall objective of this (R21) Exploratory/Developmental study is to obtain pilot data to support the feasibility and potential value of a new approach to predict mood episodes in stable adult patients with BD. This proposal aims to develop new data modeling and inference techniques that will enable more tailored clinical signal detection: examining changes within each individual, over time. To do so, we propose integrating multimodal, high-dimensional telemonitoring data, nonlinear techniques and artificial intelligence classification systems. This approach builds on our preliminary work on: (i) nonlinear techniques for the study of mood regulation in BD; (ii) an award-winning simulation using a machine learning technique (Markov Brains) for episode prediction in BD. AIMS: Aim 1 (feasibility): To obtain and integrate multimodal data to perform time-series analysis and to calculate entropy levels in 90 euthymic BD adults. Exploratory Aim 2 (potential value): To use machine learning techniques (Markov Brains) to distinguish participants at higher risk for a depressive or manic relapse based on their time-series and entropy levels (from Aim 1). HYPOTHESES: H1: We will be able to collect enough data in 80% of our participants and to integrate multimodal data to perform time-series analysis and to calculate entropy levels. H2: Markov Brains will identify participants at higher risk for a mood episode based on high (vs. low) auto-correlated time-series and low (vs. high) entropy levels. SIGNIFICANCE: This R21 application challenges more traditional prediction models by conceptualizing inter- and intra-individual variability as a dynamic property of biological systems. By leveraging densely-sampled objective and subjective data, autonomic, clinical and demographic data, this proposal aims to develop inference techniques that will examine changes within each individual, over time, in order to enhance the estimation performance. Ultimately, if we develop the capacity to predict mood episodes, we should be able to prevent them.