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Data"],"published-print":{"date-parts":[[2021,4,30]]},"abstract":"<jats:p>\n            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\u2019s NetHealth study that collected individuals\u2019 (student participants\u2019) 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\u2019 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\u2019 different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals\u2019 mental health as another problem type\u2014that 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\n            <jats:italic>non-network<\/jats:italic>\n            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\u2019 mental health conditions.\n          <\/jats:p>","DOI":"10.1145\/3429446","type":"journal-article","created":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T06:26:18Z","timestamp":1618035978000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Heterogeneous Network Approach to Predict Individuals\u2019 Mental Health"],"prefix":"10.1145","volume":"15","author":[{"given":"Shikang","family":"Liu","sequence":"first","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatemeh","family":"Vahedian","sequence":"additional","affiliation":[{"name":"University of Notre Dame and University of Michigan, Ann Arbor, MI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Hachen","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Lizardo","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Poellabauer","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron","family":"Striegel","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tijana","family":"Milenkovi\u0107","sequence":"additional","affiliation":[{"name":"Eck Institute for Global Health and University of Notre Dame, Notre Dame, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISADS.2017.41"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2674396.2674408"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.5665\/sleep.2810"},{"key":"e_1_2_2_4_1","volume-title":"Proceedings of the 7th IEEE International Conference on Data Mining (ICDM\u201907)","author":"Bader Brett W.","unstructured":"Brett W. 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