Background Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data. Conclusions These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states.
Background Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.
We introduce PyHHMM, an object-oriented open-source Python implementation of Heterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic core functionalities, such as different initialization algorithms and classical observations models, i.e., continuous and multinoulli, PyHHMM distinctively emphasizes features not supported in similar available frameworks: a heterogeneous observation model, missing data inference, different model order selection criterias, and semi-supervised training. These characteristics result in a feature-rich implementation for researchers working with sequential data. PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages, and is distributed under the Apache-2.0 License. PyHHMM's source code is publicly available on Github 1 to facilitate adoptions and future contributions. A detailed documentation 2 , which covers examples of use and models' theoretical explanation, is available. The package can be installed through the Python Package Index (PyPI).
BACKGROUND Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behaviour. The advent of eHealth and mHealth technologies enables rich sets of information to be collected from individuals in a non-invasive way presenting a promising opportunity for the construction of behavioural markers of mental health. Importantly, combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualised view of a patient's mental state than questionnaire data alone. However, in the real world, this kind of data is usually noisy and incomplete - with significant numbers of missing observations. Realising the clinical potential of mHealth tools, therefore depends critically upon the development of methods to cope with such data. OBJECTIVE Here, we present a machine learning-based approach for emotional valence (mood) analysis using passively-collected data from mobile phones and wearable devices. METHODS Passively-sensed behaviour and self-reported emotional state data from an international cohort of N=943 individuals (psychiatric outpatients recruited from community clinics) were available for analysis. All study participants had at least 30 days worth of observations of naturally-occurring behaviour, which included information about physical activity, geolocation, sleep, and smartphone app usage. These regularly sampled, but frequently missing and heterogeneous time series data were analysed using a semi-supervised Hidden Markov Model (HMM) for data averaging and feature extraction, which was then combined with a classifier to provide emotional valence predictions. We examined the performance of both a variety of classical machine learning methods and recurrent neural networks. RESULTS The best-performing models achieved greater than 0.80 Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) and 0.75 Area Under the Precision-Recall Curve (AUC-PRC) when predicting self-reported emotional valence from behaviour in held-out test data. Models which took into account the posterior probabilities of latent states identified by the HMM analysis outperformed those which did not - suggesting that the underlying behavioural patterns identified were meaningful with respect to individuals' overall emotional state. CONCLUSIONS These findings demonstrate the feasibility of designing machine learning models for predicting emotional state from mobile sensing data that are capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent a valuable tool for clinicians in the monitoring of mood states of their patients.
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