Heterogeneous network approach to predict individuals' mental health

Liu, Shikang; Vahedian, Fatemeh; Hachen, David; Ližardo, Omar; Poellabauer, Christian; Striegel, Aaron; Milenković, Tijana

doi:10.48550/arxiv.1906.04346

Cited by 4 publications

(15 citation statements)

References 80 publications

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“…In more detail, a group of studies built machine learning models to predict mental health, but they are non-network studies [7,12,17,19,34,41,47], unlike our network study. And as discussed above, we already showed that network studies are superior to non-network studies in this task [28]. Among network studies (including those that did not necessarily focus on mental health), a majority only explored the existence of a potential "correlation" between individuals' social networks and their traits (e.g., whether individuals who have different personality traits tend to have different centrality values).…”

Section: Introductionmentioning

confidence: 97%

“…Precision medicine, i.e., giving personalized health-related recommendations to individuals, has received attention both in the clinic via -omics data collection technologies [18,29] and outside the clinic via mobile health tracking devices such as smartphones or wearable sensors, online social media behavioral data, or other non-traditional personal health data resources [11,25,28,36,37,45]. In this study, we focus on the role of individuals' social interactions on their health.…”

Section: Introductionmentioning

confidence: 99%

“…Since networks capture complex wirings between entities, network studies are expected to be advantageous. Indeed, in our previous study [28], we showed that network analysis of rich social data originating from the NetHealth study [15,39] was more accurate than nonnetwork analysis of the same data in the task of predicting individuals' mental health.…”

Section: Introductionmentioning

confidence: 99%

“…In more detail, this previous study of ours [28] is the most comprehensive work to date on predicting individuals' mental health in terms of both data size (the number of considered individuals) and data heterogeneity (the number of considered data types). Namely, we leveraged the rich NetHealth data set containing individuals' social interaction data (SMS communications), healthrelated behavioral data (physical activity and sleep duration) and a variety of individuals' trait data (personality traits, social status, physical health, and well-being) to predict mental health conditions [28]. We integrated such data into a heterogeneous information network (HIN).…”

Section: Introductionmentioning

confidence: 99%

“…So, in this study, we fairly evaluate the power of using dynamic versus static social network data in the task of predicting mental health. If the former has more predictive power, our HIN-based predictive framework from our previous study [28] could be extended to incorporate dynamic instead of static social network data. Since doing this is non-trivial, as discussed above, it is out of the scope of the current paper and is instead left as future work.…”

Section: Introductionmentioning

confidence: 99%

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The power of dynamic social networks to predict individuals’ mental health

et al. 2019

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Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to develop a predictive model of one's likelihood to be depressed or anxious from rich dynamic social network data. To our knowledge, we are the first to do this. Existing studies differ from our work in at least one aspect: they do not model social interaction data as a network; they do so but analyze static network data; they examine "correlation" between social networks and health but without developing a predictive model; or they study other individual traits but not mental health. In a systematic and comprehensive evaluation, we show that our predictive model that uses dynamic social network data is superior to its static network as well as non-network equivalents when run on the same data.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The power of dynamic social networks to predict individuals’ mental health

et al. 2019

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DUGRA: Dual-Graph Representation Learning for Health Information Networks

Wang

Fung

Hung

2020

2020 IEEE International Conference on Big Data (Big Data)

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Network-based protein structural classification

Newaz¹,

Ghalehnovi²,

Rahnama³

et al. 2018

Preprint

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Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct 3-dimensional (3D) structure-based protein features. In contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of ∼9, 400 CATH and ∼12, 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times.

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Heterogeneous network approach to predict individuals' mental health

Cited by 4 publications

References 80 publications

The power of dynamic social networks to predict individuals’ mental health

The power of dynamic social networks to predict individuals’ mental health

DUGRA: Dual-Graph Representation Learning for Health Information Networks

Network-based protein structural classification

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