2018
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Abstract: Depression is a common mood disorder that causes severe medical problems and interferes negatively with daily life. Identifying human behavior patterns that are predictive or indicative of depressive disorder is important. Clinical diagnosis of depression relies on costly clinician assessment using survey instruments which may not objectively reflect the fluctuation of daily behavior. Self-administered surveys, such as the Quick Inventory of Depressive Symptomatology (QIDS) commonly used to monitor depression,… Show more

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Cited by 49 publications
(40 citation statements)
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References 36 publications
(43 reference statements)
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“…Specifically, the authors predicted the well-being for a subgroup of people who shared similar personality traits and behaviors by treating the prediction for these people as different tasks. A more recent work by Lu et al 29 developed a MTL method to jointly model sensing data collected from different smartphone platforms (Android and iOS) for depression detection, where predicting self-reported assessment scores and clinical severity of depression on each platform were considered four different tasks. Taken together, the aim of this paper is to predict more fine-grained symptom trajectories of schizophrenia in terms of patients' self-reported EMA scores using clinically meaningful rhythm features-the different types of cyclic patterns in patients' behaviors and their surrounding environment that are extracted from their passive mobile sensor data.…”
mentioning
confidence: 99%
“…Feature LOC-7, the living area size, represents an imaginary circle encompassing the various locations that a user traveled across on a particular day [21]. Canzian et al Travel distance [21], [32], [35], [37], [41], [42], [43], [44], [45], [46], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61]…”
Section: Location and Mobility Featurementioning
confidence: 99%
“…Time spent at significant location (home, clinic, office, school, etc.) [32], [39], [41], [42], [43], [47], [48], [49], [53], [55], [56], [58], [59], [60], [61], [62], [63], [64], [65], [66] LOC -3 Number of places visited [32], [37], [39], [41], [42], [48], [53], [55], [56], [57], [60], [61], [67] LOC -4 Transition time [32], [37], [41], [42], [43], [50], [61], [64], [66] LOC -5 Routine index [32], [37], [41], [42], [60] LOC-6…”
Section: Loc-2mentioning
confidence: 99%
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