Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study.
Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study.
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DOI：
10.2196/10194
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发表时间：
2018-10-22
影响因子：
7.4
通讯作者：
De Vos M
中科院分区：
文献类型：
--
作者：
Palmius N;Saunders KEA;Carr O;Geddes JR;Goodwin GM;De Vos M
精神疾病的客观行为标记通常通过智能手机或可穿戴设备记录，有可能改变精神卫生服务的提供方式，并帮助用户监测自己的健康状况。将客观标志物与疾病联系起来通常是使用人口水平模型进行的，该模型假设每个人都是一样的。现实情况是，无论是在对疾病的反应还是在通常的行为模式方面，都存在很大程度的自然个体间变异性，以及这些模型没有考虑的个体内变异性。本研究的目的是证明将人群分成个体子集的实用性，这些个体在其客观标记物和精神状态之间表现出相似的关系。使用这些子集，可以基于与他们最相似的其他个体为个体构建"群体个性化"模型。我们收集了来自59名参与者的地理位置数据，这些参与者是牛津大学症状严重程度自动监测研究的一部分。这是一项观察性数据收集研究。参与者被诊断为双相情感障碍（n = 20）;边缘型人格障碍（n = 17）;或健康对照（n = 22）。使用安装在参与者智能手机上的自定义Android应用程序收集地理位置数据，参与者每周使用16项抑郁症快速问卷报告他们的抑郁症状。研究人员建立了人群水平模型，利用从参与者记录的地理位置数据中获得的特征来估计抑郁水平，并假设可以通过将个体分为行为特征与抑郁症状之间具有相似关系的亚组来改善结果。我们开发了一个新的模型，使用Dirichlet过程将个体分成组，每组中使用贝叶斯Lasso模型将行为特征与精神疾病联系起来。其结果是为每个个体建立一个模型，该模型结合了来自其他相似个体的信息，以增强可用的有限训练数据。新的群体个性化回归模型在预测心理健康严重程度方面比人群水平模型有显着改善（P <.001）。对分组的分析表明，不同群体的特点是来自原始地理定位数据的不同特征。这项研究表明，在开发精神疾病模型时，处理个体间变异的重要性。群体水平模型不能捕捉不同个体对疾病反应的细微差别，而群体个性化模型在从客观行为特征估计心理状态时，展示了克服这些限制的潜在方法。
Objective behavioral markers of mental illness, often recorded through smartphones or wearable devices, have the potential to transform how mental health services are delivered and to help users monitor their own health. Linking objective markers to illness is commonly performed using population-level models, which assume that everyone is the same. The reality is that there are large levels of natural interindividual variability, both in terms of response to illness and in usual behavioral patterns, as well as intraindividual variability that these models do not consider. The objective of this study was to demonstrate the utility of splitting the population into subsets of individuals that exhibit similar relationships between their objective markers and their mental states. Using these subsets, “group-personalized” models can be built for individuals based on other individuals to whom they are most similar. We collected geolocation data from 59 participants who were part of the Automated Monitoring of Symptom Severity study at the University of Oxford. This was an observational data collection study. Participants were diagnosed with bipolar disorder (n=20); borderline personality disorder (n=17); or were healthy controls (n=22). Geolocation data were collected using a custom Android app installed on participants’ smartphones, and participants weekly reported their symptoms of depression using the 16-item quick inventory of depressive symptomatology questionnaire. Population-level models were built to estimate levels of depression using features derived from the geolocation data recorded from participants, and it was hypothesized that results could be improved by splitting individuals into subgroups with similar relationships between their behavioral features and depressive symptoms. We developed a new model using a Dirichlet process prior for splitting individuals into groups, with a Bayesian Lasso model in each group to link behavioral features with mental illness. The result is a model for each individual that incorporates information from other similar individuals to augment the limited training data available. The new group-personalized regression model showed a significant improvement over population-level models in predicting mental health severity (P<.001). Analysis of subgroups showed that different groups were characterized by different features derived from raw geolocation data. This study demonstrates the importance of handling interindividual variability when developing models of mental illness. Population-level models do not capture nuances in how different individuals respond to illness, and the group-personalized model demonstrates a potential way to overcome these limitations when estimating mental state from objective behavioral features.
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影响因子：
7.4
作者：
Saeb S;Zhang M;Karr CJ;Schueller SM;Corden ME;Kording KP;Mohr DC
通讯作者：
Mohr DC
影响因子：
6.8
作者：
Carr O;Saunders KEA;Bilderbeck AC;Tsanas A;Palmius N;Geddes JR;Foster R;De Vos M;Goodwin GM
通讯作者：
Goodwin GM
影响因子：
5.2
作者：
Tsanas A;Saunders K;Bilderbeck A;Palmius N;Goodwin G;De Vos M
通讯作者：
De Vos M
DOI：
10.1146/annurev-clinpsy-032816-044949
发表时间：
2017-05-08
影响因子：
18.4
作者：
Mohr DC;Zhang M;Schueller SM
通讯作者：
Schueller SM
影响因子：
2.4
作者：
Neal, RM
通讯作者：
Neal, RM