Heterogeneous Network Approach to Predict Individuals’ Mental Health

Abstract

Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous dataset from the University of Notre Dame’s NetHealth study that collected individuals’ (student participants’) social interaction data via smartphones, health-related behavioral data via wearables (Fitbit), and trait data from surveys. To integrate the different types of information, we model the NetHealth data as a heterogeneous information network (HIN). Then, we redefine the problem of predicting individuals’ mental health conditions (depression or anxiety) in a novel manner, as applying to our HIN a popular paradigm of a recommender system (RS), which is typically used to predict the preference that a person would give to an item (e.g., a movie or book). In our case, the items are the individuals’ different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals’ mental health as another problem type—that of node classification (NC) in our HIN, evaluating in the process four node features under logistic regression as a proof-of-concept classifier. We find that our RS and NC network methods produce more accurate predictions than a logistic regression model using the same NetHealth data in the traditional non-network fashion as well as a random-approach. Also, we find that the best of the considered RS approaches outperforms all considered NC approaches. This is the first study to integrate smartphone, wearable sensor, and survey data in a HIN manner and use RS or NC on the HIN to predict individuals’ mental health conditions.

[Retracted] Educational Reward and Punishment and the Effect of Psychological Intervention on Adolescent Depression

“…Mcpeak et al' s research on adolescent patients with mental illness also found that people with high menstrual quality have a tendency to develop spermatic diseases [ 4 ]. Related studies have found that adolescents with childhood trauma experience are more likely to have emotional and behavioral problems such as anxiety, suicidal ideation, drug abuse, self-harm, and aggressive behavior [ 5 ].…”

Classification Algorithm for Heterogeneous Network Data Streams Based on Big Data Active Learning

“…The algorithm was proved to outperform the baseline through simulation experiments [6]. Liu et al modeled the collected Net Health data as HIN in order to identify individuals vulnerable to depression and anxiety, and then a novel way to redefine the problem of predicting individual mental health status, and finally modeled individual mental health prediction as a node classification in HIN of another problem type by evaluating four node features as proof-of-concept classifiers in the process of logistic regression [7]. Sharma et al implemented functional encryption of letters in UAV-assisted heterogeneous networks for dense urban areas to protect data from illegal intrusion; the main goal of this technique implementation is to provide proof-of-safe passage against illegal intrusion.…”

“…In Equation (7), η is the learning rate. ∂ is the number of iterations, and θ is the randomly selected pairs of nodes from the heterogeneous network.…”

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

“…Many scholars have proposed to use deep neural network models such as CNN [ 11 ] and Bi-LSTM [ 12 ] to analyze and train the influencing factors affecting students' affective cognition problems and establish analytical models to classify and predict students' affective cognition problems. In addition, the literature [ 13 ] and the literature [ 14 ] applied the massive data recorded by the relevant systems on campus to mine the behavioral data related to students' affective cognition. Meanwhile, artificial intelligence algorithms are used to intelligently identify students' abnormal behavior and construct early warning models.…”